anonymoussubmitter222
commited on
Commit
•
e63fe3d
1
Parent(s):
7d9fedb
added app file
Browse filesThis view is limited to 50 files because it contains too many changes.
See raw diff
- .gitattributes +1 -0
- Untitled.ipynb +242 -0
- __pycache__/lm_tunisian.cpython-38.pyc +0 -0
- app.py +371 -0
- ctc_train.py +339 -0
- debugging.csv +11 -0
- file.wav +0 -0
- lm_decoded_ctc.py +376 -0
- lm_tunisian.py +361 -0
- partly_frozen_splitted_wavlm/1986/ctc_train.py +339 -0
- partly_frozen_splitted_wavlm/1986/env.log +402 -0
- partly_frozen_splitted_wavlm/1986/hyperparams.yaml +162 -0
- partly_frozen_splitted_wavlm/1986/lm_decoded_ctc.py +377 -0
- partly_frozen_splitted_wavlm/1986/lm_tunisian.py +361 -0
- partly_frozen_splitted_wavlm/1986/log.txt +0 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/CKPT.yaml +4 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/brain.ckpt +3 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/counter.ckpt +3 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/dataloader-TRAIN.ckpt +3 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/model.ckpt +3 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/modelopt.ckpt +3 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/scheduler_model.ckpt +3 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/scheduler_wav2vec.ckpt +3 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/wav2vec2.ckpt +3 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/wav2vec_opt.ckpt +3 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/CKPT.yaml +4 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/brain.ckpt +3 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/counter.ckpt +3 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/dataloader-TRAIN.ckpt +3 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/model.ckpt +3 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/modelopt.ckpt +3 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/scheduler_model.ckpt +3 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/scheduler_wav2vec.ckpt +3 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/wav2vec2.ckpt +3 -0
- partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/wav2vec_opt.ckpt +3 -0
- partly_frozen_splitted_wavlm/1986/save/label_encoder.txt +46 -0
- partly_frozen_splitted_wavlm/1986/train_log.txt +86 -0
- partly_frozen_splitted_wavlm/1986/wer_test.txt +0 -0
- partly_frozen_splitted_wavlm/1986/wer_test_salah.txt +61 -0
- partly_frozen_splitted_wavlm/1986/wer_test_salah_local.txt +21 -0
- partly_frozen_splitted_wavlm/ctc_train.py +339 -0
- partly_frozen_splitted_wavlm/env.log +379 -0
- partly_frozen_splitted_wavlm/hyperparams.yaml +162 -0
- partly_frozen_splitted_wavlm/log.txt +1998 -0
- partly_frozen_splitted_wavlm/save/label_encoder.txt +37 -0
- recording.webm +0 -0
- requirements.txt +16 -0
- running_tunisian.ipynb +0 -0
- samples/Salah1.wav +0 -0
- samples/Salah10.wav +0 -0
.gitattributes
CHANGED
@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
32 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
33 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
34 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
32 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
33 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
34 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
35 |
+
tunisian.arpa filter=lfs diff=lfs merge=lfs -text
|
Untitled.ipynb
ADDED
@@ -0,0 +1,242 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"cells": [
|
3 |
+
{
|
4 |
+
"cell_type": "code",
|
5 |
+
"execution_count": 1,
|
6 |
+
"id": "71d69be2",
|
7 |
+
"metadata": {},
|
8 |
+
"outputs": [],
|
9 |
+
"source": [
|
10 |
+
"import torchaudio\n",
|
11 |
+
"import numpy as np \n",
|
12 |
+
"import torch\n",
|
13 |
+
"import pandas as pd\n",
|
14 |
+
"import os"
|
15 |
+
]
|
16 |
+
},
|
17 |
+
{
|
18 |
+
"cell_type": "code",
|
19 |
+
"execution_count": 2,
|
20 |
+
"id": "eb5c6da2",
|
21 |
+
"metadata": {
|
22 |
+
"scrolled": true
|
23 |
+
},
|
24 |
+
"outputs": [
|
25 |
+
{
|
26 |
+
"data": {
|
27 |
+
"text/plain": [
|
28 |
+
"['Salah1.wav',\n",
|
29 |
+
" 'Salah2.wav',\n",
|
30 |
+
" 'Salah3.wav',\n",
|
31 |
+
" 'Salah4.wav',\n",
|
32 |
+
" 'Salah5.wav',\n",
|
33 |
+
" 'Salah6.wav',\n",
|
34 |
+
" 'Salah7.wav',\n",
|
35 |
+
" 'Salah8.wav',\n",
|
36 |
+
" 'Salah9.wav',\n",
|
37 |
+
" 'Salah10.wav']"
|
38 |
+
]
|
39 |
+
},
|
40 |
+
"execution_count": 2,
|
41 |
+
"metadata": {},
|
42 |
+
"output_type": "execute_result"
|
43 |
+
}
|
44 |
+
],
|
45 |
+
"source": [
|
46 |
+
"files = os.listdir(\"./\")\n",
|
47 |
+
"files = [x for x in files if \".wav\" in x]\n",
|
48 |
+
"files = [f\"Salah{i}.wav\" for i in range(1,11)]\n",
|
49 |
+
"files"
|
50 |
+
]
|
51 |
+
},
|
52 |
+
{
|
53 |
+
"cell_type": "code",
|
54 |
+
"execution_count": 4,
|
55 |
+
"id": "b2be1d8e",
|
56 |
+
"metadata": {},
|
57 |
+
"outputs": [],
|
58 |
+
"source": [
|
59 |
+
"words = {}\n",
|
60 |
+
"words[1] = \"نحب ماكلة بنينة كسكروت نظيف و رخيص\"\n",
|
61 |
+
"words[2]= \"باهي وقتاش نمشيو ال تونس\"\n",
|
62 |
+
"words[3] = \"اعطيني خمسة الاف و خمسة ميا بلاهي\"\n",
|
63 |
+
"words[4] = \"تعبت هاني راكش في الدار\"\n",
|
64 |
+
"words[5] = \"نهار السبت ماشي نقرى ان شاء الله\"\n",
|
65 |
+
"words[6]= \"زعما نلقى أحمد في الستاد ولا ماهوش هوني\"\n",
|
66 |
+
"words[7]= \"نحب نمشي ال بنزرت نرتاح شوية\"\n",
|
67 |
+
"words[8] = \"حكيت مع لولاد قالولي كل شي مريقل نهار السبت\"\n",
|
68 |
+
"words[9] = \"ناكل كفتاجي و نجم نشري شوية حوت زادة\"\n",
|
69 |
+
"words[10] = \"انتي خويا و عشيري صالح نحبك\"\n",
|
70 |
+
"words = [words[i] for i in range(1,11)]"
|
71 |
+
]
|
72 |
+
},
|
73 |
+
{
|
74 |
+
"cell_type": "code",
|
75 |
+
"execution_count": 6,
|
76 |
+
"id": "c46588ba",
|
77 |
+
"metadata": {},
|
78 |
+
"outputs": [
|
79 |
+
{
|
80 |
+
"name": "stdout",
|
81 |
+
"output_type": "stream",
|
82 |
+
"text": [
|
83 |
+
"torch.Size([1, 238080])\n",
|
84 |
+
"torch.Size([1, 184320])\n",
|
85 |
+
"torch.Size([1, 207360])\n",
|
86 |
+
"torch.Size([1, 168960])\n",
|
87 |
+
"torch.Size([1, 168960])\n",
|
88 |
+
"torch.Size([1, 192000])\n",
|
89 |
+
"torch.Size([1, 184320])\n",
|
90 |
+
"torch.Size([1, 199680])\n",
|
91 |
+
"torch.Size([1, 230400])\n",
|
92 |
+
"torch.Size([1, 192000])\n"
|
93 |
+
]
|
94 |
+
}
|
95 |
+
],
|
96 |
+
"source": [
|
97 |
+
"durations= []\n",
|
98 |
+
"path_jz = \"samples/\"\n",
|
99 |
+
"paths = [os.path.join(path_jz,x) for x in files]\n",
|
100 |
+
"srs= [48000 for x in paths]\n",
|
101 |
+
"IDs=[]\n",
|
102 |
+
"for f in files: \n",
|
103 |
+
" x,sr = torchaudio.load(f)\n",
|
104 |
+
" new_audio = torch.mean(x, dim=0).unsqueeze(0)\n",
|
105 |
+
" print(new_audio.shape)\n",
|
106 |
+
" torchaudio.save(os.path.join(\"monoaudiotun\", f), new_audio, sr)\n",
|
107 |
+
" duration = float(x.shape[1]) / sr\n",
|
108 |
+
" durations.append(duration)\n",
|
109 |
+
" IDs.append(f.split(\".\")[0])\n",
|
110 |
+
" \n"
|
111 |
+
]
|
112 |
+
},
|
113 |
+
{
|
114 |
+
"cell_type": "code",
|
115 |
+
"execution_count": 7,
|
116 |
+
"id": "b71db098",
|
117 |
+
"metadata": {},
|
118 |
+
"outputs": [],
|
119 |
+
"source": [
|
120 |
+
"test_salah = pd.DataFrame(\n",
|
121 |
+
" {'ID': IDs,\n",
|
122 |
+
" 'duration': durations,\n",
|
123 |
+
" 'wav': paths,\n",
|
124 |
+
" \"sr\": srs,\n",
|
125 |
+
" \"wrd\": words\n",
|
126 |
+
" })\n"
|
127 |
+
]
|
128 |
+
},
|
129 |
+
{
|
130 |
+
"cell_type": "code",
|
131 |
+
"execution_count": 8,
|
132 |
+
"id": "b3fdd365",
|
133 |
+
"metadata": {},
|
134 |
+
"outputs": [],
|
135 |
+
"source": [
|
136 |
+
"test_salah.to_csv(\"test_salah_local.csv\", index=False)"
|
137 |
+
]
|
138 |
+
},
|
139 |
+
{
|
140 |
+
"cell_type": "code",
|
141 |
+
"execution_count": 28,
|
142 |
+
"id": "f6ac8451",
|
143 |
+
"metadata": {},
|
144 |
+
"outputs": [
|
145 |
+
{
|
146 |
+
"name": "stdout",
|
147 |
+
"output_type": "stream",
|
148 |
+
"text": [
|
149 |
+
"%WER 45.59 [ 31 / 68, 3 ins, 7 del, 21 sub ]\n",
|
150 |
+
"%SER 90.00 [ 9 / 10 ]\n",
|
151 |
+
"Scored 10 sentences, 0 not present in hyp.\n",
|
152 |
+
"================================================================================\n",
|
153 |
+
"ALIGNMENTS\n",
|
154 |
+
"\n",
|
155 |
+
"Format:\n",
|
156 |
+
"<utterance-id>, WER DETAILS\n",
|
157 |
+
"<eps> ; reference ; on ; the ; first ; line\n",
|
158 |
+
" I ; S ; = ; = ; S ; D \n",
|
159 |
+
" and ; hypothesis ; on ; the ; third ; <eps>\n",
|
160 |
+
"================================================================================\n",
|
161 |
+
"Salah4, %WER 0.00 [ 0 / 5, 0 ins, 0 del, 0 sub ]\n",
|
162 |
+
"تعبت ; هاني ; راكش ; في ; الدار\n",
|
163 |
+
" = ; = ; = ; = ; = \n",
|
164 |
+
"تعبت ; هاني ; راكش ; في ; الدار\n",
|
165 |
+
"================================================================================\n",
|
166 |
+
"Salah5, %WER 57.14 [ 4 / 7, 0 ins, 1 del, 3 sub ]\n",
|
167 |
+
"نهار ; السبت ; ماشي ; نقرى ; ان ; شاء ; ا��له\n",
|
168 |
+
" = ; = ; = ; S ; S ; S ; D \n",
|
169 |
+
"نهار ; السبت ; ماشي ; نقرا ; إن ; شاءالله ; <eps>\n",
|
170 |
+
"================================================================================\n",
|
171 |
+
"Salah2, %WER 60.00 [ 3 / 5, 0 ins, 1 del, 2 sub ]\n",
|
172 |
+
"باهي ; وقتاش ; نمشيو ; ال ; تونس\n",
|
173 |
+
" = ; = ; S ; S ; D \n",
|
174 |
+
"باهي ; وقتاش ; نمشيوا ; لتونس ; <eps>\n",
|
175 |
+
"================================================================================\n",
|
176 |
+
"Salah7, %WER 33.33 [ 2 / 6, 0 ins, 1 del, 1 sub ]\n",
|
177 |
+
"نحب ; نمشي ; ال ; بنزرت ; نرتاح ; شوية\n",
|
178 |
+
" = ; = ; S ; D ; = ; = \n",
|
179 |
+
"نحب ; نمشي ; لبنزرت ; <eps> ; نرتاح ; شوية\n",
|
180 |
+
"================================================================================\n",
|
181 |
+
"Salah6, %WER 37.50 [ 3 / 8, 0 ins, 0 del, 3 sub ]\n",
|
182 |
+
"زعما ; نلقى ; أحمد ; في ; الستاد ; ولا ; ماهوش ; هوني\n",
|
183 |
+
" S ; = ; = ; = ; S ; S ; = ; = \n",
|
184 |
+
"زعمة ; نلقى ; أحمد ; في ; السعد ; وإلا ; ماهوش ; هوني\n",
|
185 |
+
"================================================================================\n",
|
186 |
+
"Salah10, %WER 83.33 [ 5 / 6, 1 ins, 1 del, 3 sub ]\n",
|
187 |
+
"انتي ; <eps> ; خويا ; و ; عشيري ; صالح ; نحبك\n",
|
188 |
+
" S ; I ; = ; S ; S ; D ; = \n",
|
189 |
+
"إنت ; ي ; خويا ; وعشيلي ; صلاح ; <eps> ; نحبك\n",
|
190 |
+
"================================================================================\n",
|
191 |
+
"Salah8, %WER 44.44 [ 4 / 9, 2 ins, 0 del, 2 sub ]\n",
|
192 |
+
"حكيت ; مع ; لولاد ; قالولي ; كل ; شي ; مريقل ; <eps> ; <eps> ; نهار ; السبت\n",
|
193 |
+
" = ; = ; S ; = ; = ; = ; S ; I ; I ; = ; = \n",
|
194 |
+
"حكيت ; مع ; الأولاد ; قالولي ; كل ; شي ; مر ; ي ; ل ; نهار ; السبت\n",
|
195 |
+
"================================================================================\n",
|
196 |
+
"Salah3, %WER 85.71 [ 6 / 7, 0 ins, 1 del, 5 sub ]\n",
|
197 |
+
"اعطيني ; خمسة ; الاف ; و ; خمسة ; ميا ; بلاهي\n",
|
198 |
+
" S ; = ; S ; S ; S ; S ; D \n",
|
199 |
+
"أعطيني ; خمسة ; آلاف ; وخمسة ; ملا ; باللاهي ; <eps>\n",
|
200 |
+
"================================================================================\n",
|
201 |
+
"Salah9, %WER 25.00 [ 2 / 8, 0 ins, 1 del, 1 sub ]\n",
|
202 |
+
"ناكل ; كفتاجي ; و ; نجم ; نشري ; شوية ; حوت ; زادة\n",
|
203 |
+
" = ; = ; S ; D ; = ; = ; = ; = \n",
|
204 |
+
"ناكل ; كفتاجي ; وننجم ; <eps> ; نشري ; شوية ; حوت ; زادة\n",
|
205 |
+
"================================================================================\n",
|
206 |
+
"Salah1, %WER 28.57 [ 2 / 7, 0 ins, 1 del, 1 sub ]\n",
|
207 |
+
"نحب ; ماكلة ; بنينة ; كسكروت ; نظيف ; و ; رخيص\n",
|
208 |
+
" = ; = ; = ; = ; = ; S ; D \n",
|
209 |
+
"نحب ; ماكلة ; بنينة ; كسكروت ; نظيف ; ورخيص ; <eps>\n"
|
210 |
+
]
|
211 |
+
}
|
212 |
+
],
|
213 |
+
"source": [
|
214 |
+
"filein = \"wer_test_salah.txt\"\n",
|
215 |
+
"with open(filein, \"r\") as wer : \n",
|
216 |
+
" lines = wer.read().splitlines()\n",
|
217 |
+
" print(\"\\n\".join(lines))"
|
218 |
+
]
|
219 |
+
}
|
220 |
+
],
|
221 |
+
"metadata": {
|
222 |
+
"kernelspec": {
|
223 |
+
"display_name": "Python 3",
|
224 |
+
"language": "python",
|
225 |
+
"name": "python3"
|
226 |
+
},
|
227 |
+
"language_info": {
|
228 |
+
"codemirror_mode": {
|
229 |
+
"name": "ipython",
|
230 |
+
"version": 3
|
231 |
+
},
|
232 |
+
"file_extension": ".py",
|
233 |
+
"mimetype": "text/x-python",
|
234 |
+
"name": "python",
|
235 |
+
"nbconvert_exporter": "python",
|
236 |
+
"pygments_lexer": "ipython3",
|
237 |
+
"version": "3.8.5"
|
238 |
+
}
|
239 |
+
},
|
240 |
+
"nbformat": 4,
|
241 |
+
"nbformat_minor": 5
|
242 |
+
}
|
__pycache__/lm_tunisian.cpython-38.pyc
ADDED
Binary file (9.51 kB). View file
|
|
app.py
ADDED
@@ -0,0 +1,371 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import sys
|
3 |
+
import torch
|
4 |
+
import logging
|
5 |
+
import speechbrain as sb
|
6 |
+
from speechbrain.utils.distributed import run_on_main
|
7 |
+
from hyperpyyaml import load_hyperpyyaml
|
8 |
+
from pathlib import Path
|
9 |
+
import torchaudio.transforms as T
|
10 |
+
import torchaudio
|
11 |
+
import numpy as np
|
12 |
+
|
13 |
+
from pyctcdecode import build_ctcdecoder
|
14 |
+
hparams_file, run_opts, overrides = sb.parse_arguments(["wavlm_partly_frozen.yaml"])
|
15 |
+
|
16 |
+
# If distributed_launch=True then
|
17 |
+
# create ddp_group with the right communication protocol
|
18 |
+
sb.utils.distributed.ddp_init_group(run_opts)
|
19 |
+
|
20 |
+
with open(hparams_file) as fin:
|
21 |
+
hparams = load_hyperpyyaml(fin, overrides)
|
22 |
+
|
23 |
+
# Create experiment directory
|
24 |
+
sb.create_experiment_directory(
|
25 |
+
experiment_directory=hparams["output_folder"],
|
26 |
+
hyperparams_to_save=hparams_file,
|
27 |
+
overrides=overrides,
|
28 |
+
)
|
29 |
+
def read_labels_file(labels_file):
|
30 |
+
with open(labels_file, "r") as lf:
|
31 |
+
lines = lf.read().splitlines()
|
32 |
+
division = "==="
|
33 |
+
numbers = {}
|
34 |
+
for line in lines :
|
35 |
+
if division in line :
|
36 |
+
break
|
37 |
+
string, number = line.split("=>")
|
38 |
+
number = int(number)
|
39 |
+
string = string[1:-2]
|
40 |
+
numbers[number] = string
|
41 |
+
return [numbers[x] for x in range(len(numbers))]
|
42 |
+
labels = read_labels_file(os.path.join(hparams["save_folder"], "label_encoder.txt"))
|
43 |
+
print(labels)
|
44 |
+
labels = [""] + labels[1:]
|
45 |
+
print(len(labels))
|
46 |
+
|
47 |
+
# Dataset prep (parsing Librispeech)
|
48 |
+
|
49 |
+
resampler_8000 = T.Resample(8000, 16000, dtype=torch.float)
|
50 |
+
|
51 |
+
resampler_44100 =T.Resample(44100, 16000, dtype=torch.float)
|
52 |
+
resampler_48000 =T.Resample(48000, 16000, dtype=torch.float)
|
53 |
+
|
54 |
+
|
55 |
+
resamplers = {"8000": resampler_8000, "44100":resampler_44100, "48000": resampler_48000}
|
56 |
+
def dataio_prepare(hparams):
|
57 |
+
"""This function prepares the datasets to be used in the brain class.
|
58 |
+
It also defines the data processing pipeline through user-defined functions."""
|
59 |
+
data_folder = hparams["data_folder"]
|
60 |
+
|
61 |
+
train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
62 |
+
csv_path=hparams["train_csv"], replacements={"data_root": data_folder},
|
63 |
+
)
|
64 |
+
|
65 |
+
if hparams["sorting"] == "ascending":
|
66 |
+
# we sort training data to speed up training and get better results.
|
67 |
+
train_data = train_data.filtered_sorted(sort_key="duration")
|
68 |
+
# when sorting do not shuffle in dataloader ! otherwise is pointless
|
69 |
+
hparams["train_dataloader_opts"]["shuffle"] = False
|
70 |
+
|
71 |
+
elif hparams["sorting"] == "descending":
|
72 |
+
train_data = train_data.filtered_sorted(
|
73 |
+
sort_key="duration", reverse=True
|
74 |
+
)
|
75 |
+
# when sorting do not shuffle in dataloader ! otherwise is pointless
|
76 |
+
hparams["train_dataloader_opts"]["shuffle"] = False
|
77 |
+
|
78 |
+
elif hparams["sorting"] == "random":
|
79 |
+
pass
|
80 |
+
|
81 |
+
else:
|
82 |
+
raise NotImplementedError(
|
83 |
+
"sorting must be random, ascending or descending"
|
84 |
+
)
|
85 |
+
|
86 |
+
valid_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
87 |
+
csv_path=hparams["valid_csv"], replacements={"data_root": data_folder},
|
88 |
+
)
|
89 |
+
valid_data = valid_data.filtered_sorted(sort_key="duration")
|
90 |
+
|
91 |
+
# test is separate
|
92 |
+
test_datasets = {}
|
93 |
+
for csv_file in hparams["test_csv"]:
|
94 |
+
name = Path(csv_file).stem
|
95 |
+
test_datasets[name] = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
96 |
+
csv_path=csv_file, replacements={"data_root": data_folder}
|
97 |
+
)
|
98 |
+
test_datasets[name] = test_datasets[name].filtered_sorted(
|
99 |
+
sort_key="duration"
|
100 |
+
)
|
101 |
+
|
102 |
+
datasets = [train_data, valid_data] + [i for k, i in test_datasets.items()]
|
103 |
+
|
104 |
+
# 2. Define audio pipeline:
|
105 |
+
@sb.utils.data_pipeline.takes("wav", "sr")
|
106 |
+
@sb.utils.data_pipeline.provides("sig")
|
107 |
+
def audio_pipeline(wav, sr):
|
108 |
+
sig = sb.dataio.dataio.read_audio(wav)
|
109 |
+
sig = resamplers[sr](sig)
|
110 |
+
return sig
|
111 |
+
|
112 |
+
sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline)
|
113 |
+
label_encoder = sb.dataio.encoder.CTCTextEncoder()
|
114 |
+
|
115 |
+
# 3. Define text pipeline:
|
116 |
+
@sb.utils.data_pipeline.takes("wrd")
|
117 |
+
@sb.utils.data_pipeline.provides(
|
118 |
+
"wrd", "char_list", "tokens_list", "tokens_bos", "tokens_eos", "tokens"
|
119 |
+
)
|
120 |
+
def text_pipeline(wrd):
|
121 |
+
yield wrd
|
122 |
+
char_list = list(wrd)
|
123 |
+
yield char_list
|
124 |
+
tokens_list = label_encoder.encode_sequence(char_list)
|
125 |
+
yield tokens_list
|
126 |
+
tokens_bos = torch.LongTensor([hparams["bos_index"]] + (tokens_list))
|
127 |
+
yield tokens_bos
|
128 |
+
tokens_eos = torch.LongTensor(tokens_list + [hparams["eos_index"]])
|
129 |
+
yield tokens_eos
|
130 |
+
tokens = torch.LongTensor(tokens_list)
|
131 |
+
yield tokens
|
132 |
+
|
133 |
+
sb.dataio.dataset.add_dynamic_item(datasets, text_pipeline)
|
134 |
+
|
135 |
+
lab_enc_file = os.path.join(hparams["save_folder"], "label_encoder.txt")
|
136 |
+
special_labels = {
|
137 |
+
"bos_label": hparams["bos_index"],
|
138 |
+
"eos_label": hparams["eos_index"],
|
139 |
+
"blank_label": hparams["blank_index"],
|
140 |
+
}
|
141 |
+
label_encoder.load_or_create(
|
142 |
+
path=lab_enc_file,
|
143 |
+
from_didatasets=[train_data],
|
144 |
+
output_key="char_list",
|
145 |
+
special_labels=special_labels,
|
146 |
+
sequence_input=True,
|
147 |
+
)
|
148 |
+
|
149 |
+
# 4. Set output:
|
150 |
+
sb.dataio.dataset.set_output_keys(
|
151 |
+
datasets,
|
152 |
+
["id", "sig", "wrd", "char_list", "tokens_bos", "tokens_eos", "tokens"],
|
153 |
+
)
|
154 |
+
return train_data, valid_data, test_datasets, label_encoder
|
155 |
+
|
156 |
+
|
157 |
+
class ASR(sb.Brain):
|
158 |
+
def compute_forward(self, batch, stage):
|
159 |
+
"""Forward computations from the waveform batches to the output probabilities."""
|
160 |
+
batch = batch.to(self.device)
|
161 |
+
wavs, wav_lens = batch.sig
|
162 |
+
print(wavs)
|
163 |
+
tokens_bos, _ = batch.tokens_bos
|
164 |
+
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
|
165 |
+
|
166 |
+
# Forward pass
|
167 |
+
feats = self.modules.wav2vec2(wavs)
|
168 |
+
x = self.modules.enc(feats)
|
169 |
+
# Compute outputs
|
170 |
+
p_tokens = None
|
171 |
+
logits = self.modules.ctc_lin(x)
|
172 |
+
p_ctc = self.hparams.log_softmax(logits)
|
173 |
+
if stage != sb.Stage.TRAIN:
|
174 |
+
p_tokens = sb.decoders.ctc_greedy_decode(
|
175 |
+
p_ctc, wav_lens, blank_id=self.hparams.blank_index
|
176 |
+
)
|
177 |
+
return p_ctc, wav_lens, p_tokens
|
178 |
+
|
179 |
+
def treat_wav(self,sig):
|
180 |
+
feats = self.modules.wav2vec2(sig.to(self.device))
|
181 |
+
x = self.modules.enc(feats)
|
182 |
+
p_tokens = None
|
183 |
+
logits = self.modules.ctc_lin(x)
|
184 |
+
p_ctc = self.hparams.log_softmax(logits)
|
185 |
+
predicted_words =[]
|
186 |
+
for logs in p_ctc:
|
187 |
+
text = decoder.decode(logs.detach().cpu().numpy())
|
188 |
+
predicted_words.append(text.split(" "))
|
189 |
+
return " ".join(predicted_words[0])
|
190 |
+
|
191 |
+
|
192 |
+
|
193 |
+
|
194 |
+
def compute_objectives(self, predictions, batch, stage):
|
195 |
+
"""Computes the loss (CTC+NLL) given predictions and targets."""
|
196 |
+
|
197 |
+
p_ctc, wav_lens, predicted_tokens = predictions
|
198 |
+
|
199 |
+
ids = batch.id
|
200 |
+
tokens_eos, tokens_eos_lens = batch.tokens_eos
|
201 |
+
tokens, tokens_lens = batch.tokens
|
202 |
+
|
203 |
+
if hasattr(self.modules, "env_corrupt") and stage == sb.Stage.TRAIN:
|
204 |
+
tokens_eos = torch.cat([tokens_eos, tokens_eos], dim=0)
|
205 |
+
tokens_eos_lens = torch.cat(
|
206 |
+
[tokens_eos_lens, tokens_eos_lens], dim=0
|
207 |
+
)
|
208 |
+
tokens = torch.cat([tokens, tokens], dim=0)
|
209 |
+
tokens_lens = torch.cat([tokens_lens, tokens_lens], dim=0)
|
210 |
+
|
211 |
+
loss_ctc = self.hparams.ctc_cost(p_ctc, tokens, wav_lens, tokens_lens)
|
212 |
+
loss = loss_ctc
|
213 |
+
if stage != sb.Stage.TRAIN:
|
214 |
+
# Decode token terms to words
|
215 |
+
predicted_words =[]
|
216 |
+
for logs in p_ctc:
|
217 |
+
text = decoder.decode(logs.detach().cpu().numpy())
|
218 |
+
predicted_words.append(text.split(" "))
|
219 |
+
|
220 |
+
target_words = [wrd.split(" ") for wrd in batch.wrd]
|
221 |
+
self.wer_metric.append(ids, predicted_words, target_words)
|
222 |
+
self.cer_metric.append(ids, predicted_words, target_words)
|
223 |
+
|
224 |
+
return loss
|
225 |
+
|
226 |
+
def fit_batch(self, batch):
|
227 |
+
"""Train the parameters given a single batch in input"""
|
228 |
+
predictions = self.compute_forward(batch, sb.Stage.TRAIN)
|
229 |
+
loss = self.compute_objectives(predictions, batch, sb.Stage.TRAIN)
|
230 |
+
loss.backward()
|
231 |
+
if self.check_gradients(loss):
|
232 |
+
self.wav2vec_optimizer.step()
|
233 |
+
self.model_optimizer.step()
|
234 |
+
|
235 |
+
self.wav2vec_optimizer.zero_grad()
|
236 |
+
self.model_optimizer.zero_grad()
|
237 |
+
|
238 |
+
return loss.detach()
|
239 |
+
|
240 |
+
def evaluate_batch(self, batch, stage):
|
241 |
+
"""Computations needed for validation/test batches"""
|
242 |
+
predictions = self.compute_forward(batch, stage=stage)
|
243 |
+
with torch.no_grad():
|
244 |
+
loss = self.compute_objectives(predictions, batch, stage=stage)
|
245 |
+
return loss.detach()
|
246 |
+
|
247 |
+
def on_stage_start(self, stage, epoch):
|
248 |
+
"""Gets called at the beginning of each epoch"""
|
249 |
+
if stage != sb.Stage.TRAIN:
|
250 |
+
self.cer_metric = self.hparams.cer_computer()
|
251 |
+
self.wer_metric = self.hparams.error_rate_computer()
|
252 |
+
|
253 |
+
def on_stage_end(self, stage, stage_loss, epoch):
|
254 |
+
"""Gets called at the end of an epoch."""
|
255 |
+
# Compute/store important stats
|
256 |
+
stage_stats = {"loss": stage_loss}
|
257 |
+
if stage == sb.Stage.TRAIN:
|
258 |
+
self.train_stats = stage_stats
|
259 |
+
else:
|
260 |
+
stage_stats["CER"] = self.cer_metric.summarize("error_rate")
|
261 |
+
stage_stats["WER"] = self.wer_metric.summarize("error_rate")
|
262 |
+
|
263 |
+
# Perform end-of-iteration things, like annealing, logging, etc.
|
264 |
+
if stage == sb.Stage.VALID:
|
265 |
+
old_lr_model, new_lr_model = self.hparams.lr_annealing_model(
|
266 |
+
stage_stats["loss"]
|
267 |
+
)
|
268 |
+
old_lr_wav2vec, new_lr_wav2vec = self.hparams.lr_annealing_wav2vec(
|
269 |
+
stage_stats["loss"]
|
270 |
+
)
|
271 |
+
sb.nnet.schedulers.update_learning_rate(
|
272 |
+
self.model_optimizer, new_lr_model
|
273 |
+
)
|
274 |
+
sb.nnet.schedulers.update_learning_rate(
|
275 |
+
self.wav2vec_optimizer, new_lr_wav2vec
|
276 |
+
)
|
277 |
+
self.hparams.train_logger.log_stats(
|
278 |
+
stats_meta={
|
279 |
+
"epoch": epoch,
|
280 |
+
"lr_model": old_lr_model,
|
281 |
+
"lr_wav2vec": old_lr_wav2vec,
|
282 |
+
},
|
283 |
+
train_stats=self.train_stats,
|
284 |
+
valid_stats=stage_stats,
|
285 |
+
)
|
286 |
+
self.checkpointer.save_and_keep_only(
|
287 |
+
meta={"WER": stage_stats["WER"]}, min_keys=["WER"],
|
288 |
+
)
|
289 |
+
elif stage == sb.Stage.TEST:
|
290 |
+
self.hparams.train_logger.log_stats(
|
291 |
+
stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
|
292 |
+
test_stats=stage_stats,
|
293 |
+
)
|
294 |
+
with open(self.hparams.wer_file, "w") as w:
|
295 |
+
self.wer_metric.write_stats(w)
|
296 |
+
|
297 |
+
def init_optimizers(self):
|
298 |
+
"Initializes the wav2vec2 optimizer and model optimizer"
|
299 |
+
self.wav2vec_optimizer = self.hparams.wav2vec_opt_class(
|
300 |
+
self.modules.wav2vec2.parameters()
|
301 |
+
)
|
302 |
+
self.model_optimizer = self.hparams.model_opt_class(
|
303 |
+
self.hparams.model.parameters()
|
304 |
+
)
|
305 |
+
|
306 |
+
if self.checkpointer is not None:
|
307 |
+
self.checkpointer.add_recoverable(
|
308 |
+
"wav2vec_opt", self.wav2vec_optimizer
|
309 |
+
)
|
310 |
+
self.checkpointer.add_recoverable("modelopt", self.model_optimizer)
|
311 |
+
|
312 |
+
label_encoder = sb.dataio.encoder.CTCTextEncoder()
|
313 |
+
|
314 |
+
train_data, valid_data, test_datasets, label_encoder = dataio_prepare(
|
315 |
+
hparams
|
316 |
+
)
|
317 |
+
|
318 |
+
|
319 |
+
# We dynamicaly add the tokenizer to our brain class.
|
320 |
+
# NB: This tokenizer corresponds to the one used for the LM!!
|
321 |
+
decoder = build_ctcdecoder(
|
322 |
+
labels,
|
323 |
+
kenlm_model_path="tunisian.arpa", # either .arpa or .bin file
|
324 |
+
alpha=0.5, # tuned on a val set
|
325 |
+
beta=1, # tuned on a val set
|
326 |
+
)
|
327 |
+
|
328 |
+
asr_brain = ASR(
|
329 |
+
modules=hparams["modules"],
|
330 |
+
hparams=hparams,
|
331 |
+
run_opts=run_opts,
|
332 |
+
checkpointer=hparams["checkpointer"],
|
333 |
+
)
|
334 |
+
asr_brain.device= "cpu"
|
335 |
+
asr_brain.modules.to("cpu")
|
336 |
+
asr_brain.tokenizer = label_encoder
|
337 |
+
|
338 |
+
from enum import Enum, auto
|
339 |
+
class Stage(Enum):
|
340 |
+
TRAIN = auto()
|
341 |
+
VALID = auto()
|
342 |
+
TEST = auto()
|
343 |
+
|
344 |
+
asr_brain.on_evaluate_start()
|
345 |
+
asr_brain.modules.eval()
|
346 |
+
import gradio as gr
|
347 |
+
def treat_wav_file(file_mic, file_upload, resamplers = resamplers,asr=asr_brain, device="cpu") :
|
348 |
+
|
349 |
+
if (file_mic is not None) and (file_upload is not None):
|
350 |
+
warn_output = "WARNING: You've uploaded an audio file and used the microphone. The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
|
351 |
+
wav = file_mic
|
352 |
+
elif (file_mic is None) and (file_upload is None):
|
353 |
+
return "ERROR: You have to either use the microphone or upload an audio file"
|
354 |
+
elif file_mic is not None:
|
355 |
+
wav = file_mic
|
356 |
+
else:
|
357 |
+
wav = file_upload
|
358 |
+
sig, sr = torchaudio.load(wav)
|
359 |
+
tensor_wav = sig.to(device)
|
360 |
+
resampled = resamplers[str(sr)](tensor_wav)
|
361 |
+
sentence = asr_brain.treat_wav(resampled)
|
362 |
+
return sentence
|
363 |
+
|
364 |
+
gr.Interface(
|
365 |
+
fn=treat_wav_file,
|
366 |
+
inputs=[gr.inputs.Audio(source="microphone", type='filepath', optional=True),
|
367 |
+
gr.inputs.Audio(source="upload", type='filepath', optional=True)]
|
368 |
+
,outputs="text").launch()
|
369 |
+
|
370 |
+
|
371 |
+
|
ctc_train.py
ADDED
@@ -0,0 +1,339 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env/python3
|
2 |
+
"""Recipe for training a wav2vec-based ctc ASR system with librispeech.
|
3 |
+
The system employs wav2vec as its encoder. Decoding is performed with
|
4 |
+
ctc greedy decoder.
|
5 |
+
To run this recipe, do the following:
|
6 |
+
> python train_with_wav2vec.py hparams/train_with_wav2vec.yaml
|
7 |
+
The neural network is trained on CTC likelihood target and character units
|
8 |
+
are used as basic recognition tokens. Training is performed on the full
|
9 |
+
LibriSpeech dataset (960 h).
|
10 |
+
|
11 |
+
Authors
|
12 |
+
* Sung-Lin Yeh 2021
|
13 |
+
* Titouan Parcollet 2021
|
14 |
+
* Ju-Chieh Chou 2020
|
15 |
+
* Mirco Ravanelli 2020
|
16 |
+
* Abdel Heba 2020
|
17 |
+
* Peter Plantinga 2020
|
18 |
+
* Samuele Cornell 2020
|
19 |
+
"""
|
20 |
+
|
21 |
+
import os
|
22 |
+
import sys
|
23 |
+
import torch
|
24 |
+
import logging
|
25 |
+
import speechbrain as sb
|
26 |
+
from speechbrain.utils.distributed import run_on_main
|
27 |
+
from hyperpyyaml import load_hyperpyyaml
|
28 |
+
from pathlib import Path
|
29 |
+
import torchaudio.transforms as T
|
30 |
+
logger = logging.getLogger(__name__)
|
31 |
+
|
32 |
+
# Define training procedure
|
33 |
+
class ASR(sb.Brain):
|
34 |
+
def compute_forward(self, batch, stage):
|
35 |
+
"""Forward computations from the waveform batches to the output probabilities."""
|
36 |
+
batch = batch.to(self.device)
|
37 |
+
wavs, wav_lens = batch.sig
|
38 |
+
tokens_bos, _ = batch.tokens_bos
|
39 |
+
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
|
40 |
+
|
41 |
+
# Forward pass
|
42 |
+
feats = self.modules.wav2vec2(wavs)
|
43 |
+
x = self.modules.enc(feats)
|
44 |
+
# Compute outputs
|
45 |
+
p_tokens = None
|
46 |
+
logits = self.modules.ctc_lin(x)
|
47 |
+
p_ctc = self.hparams.log_softmax(logits)
|
48 |
+
if stage != sb.Stage.TRAIN:
|
49 |
+
p_tokens = sb.decoders.ctc_greedy_decode(
|
50 |
+
p_ctc, wav_lens, blank_id=self.hparams.blank_index
|
51 |
+
)
|
52 |
+
return p_ctc, wav_lens, p_tokens
|
53 |
+
|
54 |
+
def compute_objectives(self, predictions, batch, stage):
|
55 |
+
"""Computes the loss (CTC+NLL) given predictions and targets."""
|
56 |
+
|
57 |
+
p_ctc, wav_lens, predicted_tokens = predictions
|
58 |
+
|
59 |
+
ids = batch.id
|
60 |
+
tokens_eos, tokens_eos_lens = batch.tokens_eos
|
61 |
+
tokens, tokens_lens = batch.tokens
|
62 |
+
|
63 |
+
if hasattr(self.modules, "env_corrupt") and stage == sb.Stage.TRAIN:
|
64 |
+
tokens_eos = torch.cat([tokens_eos, tokens_eos], dim=0)
|
65 |
+
tokens_eos_lens = torch.cat(
|
66 |
+
[tokens_eos_lens, tokens_eos_lens], dim=0
|
67 |
+
)
|
68 |
+
tokens = torch.cat([tokens, tokens], dim=0)
|
69 |
+
tokens_lens = torch.cat([tokens_lens, tokens_lens], dim=0)
|
70 |
+
|
71 |
+
loss_ctc = self.hparams.ctc_cost(p_ctc, tokens, wav_lens, tokens_lens)
|
72 |
+
loss = loss_ctc
|
73 |
+
|
74 |
+
if stage != sb.Stage.TRAIN:
|
75 |
+
# Decode token terms to words
|
76 |
+
predicted_words = [
|
77 |
+
"".join(self.tokenizer.decode_ndim(utt_seq)).split(" ")
|
78 |
+
for utt_seq in predicted_tokens
|
79 |
+
]
|
80 |
+
target_words = [wrd.split(" ") for wrd in batch.wrd]
|
81 |
+
self.wer_metric.append(ids, predicted_words, target_words)
|
82 |
+
self.cer_metric.append(ids, predicted_words, target_words)
|
83 |
+
|
84 |
+
return loss
|
85 |
+
|
86 |
+
def fit_batch(self, batch):
|
87 |
+
"""Train the parameters given a single batch in input"""
|
88 |
+
predictions = self.compute_forward(batch, sb.Stage.TRAIN)
|
89 |
+
loss = self.compute_objectives(predictions, batch, sb.Stage.TRAIN)
|
90 |
+
loss.backward()
|
91 |
+
if self.check_gradients(loss):
|
92 |
+
self.wav2vec_optimizer.step()
|
93 |
+
self.model_optimizer.step()
|
94 |
+
|
95 |
+
self.wav2vec_optimizer.zero_grad()
|
96 |
+
self.model_optimizer.zero_grad()
|
97 |
+
|
98 |
+
return loss.detach()
|
99 |
+
|
100 |
+
def evaluate_batch(self, batch, stage):
|
101 |
+
"""Computations needed for validation/test batches"""
|
102 |
+
predictions = self.compute_forward(batch, stage=stage)
|
103 |
+
with torch.no_grad():
|
104 |
+
loss = self.compute_objectives(predictions, batch, stage=stage)
|
105 |
+
return loss.detach()
|
106 |
+
|
107 |
+
def on_stage_start(self, stage, epoch):
|
108 |
+
"""Gets called at the beginning of each epoch"""
|
109 |
+
if stage != sb.Stage.TRAIN:
|
110 |
+
self.cer_metric = self.hparams.cer_computer()
|
111 |
+
self.wer_metric = self.hparams.error_rate_computer()
|
112 |
+
|
113 |
+
def on_stage_end(self, stage, stage_loss, epoch):
|
114 |
+
"""Gets called at the end of an epoch."""
|
115 |
+
# Compute/store important stats
|
116 |
+
stage_stats = {"loss": stage_loss}
|
117 |
+
if stage == sb.Stage.TRAIN:
|
118 |
+
self.train_stats = stage_stats
|
119 |
+
else:
|
120 |
+
stage_stats["CER"] = self.cer_metric.summarize("error_rate")
|
121 |
+
stage_stats["WER"] = self.wer_metric.summarize("error_rate")
|
122 |
+
|
123 |
+
# Perform end-of-iteration things, like annealing, logging, etc.
|
124 |
+
if stage == sb.Stage.VALID:
|
125 |
+
old_lr_model, new_lr_model = self.hparams.lr_annealing_model(
|
126 |
+
stage_stats["loss"]
|
127 |
+
)
|
128 |
+
old_lr_wav2vec, new_lr_wav2vec = self.hparams.lr_annealing_wav2vec(
|
129 |
+
stage_stats["loss"]
|
130 |
+
)
|
131 |
+
sb.nnet.schedulers.update_learning_rate(
|
132 |
+
self.model_optimizer, new_lr_model
|
133 |
+
)
|
134 |
+
sb.nnet.schedulers.update_learning_rate(
|
135 |
+
self.wav2vec_optimizer, new_lr_wav2vec
|
136 |
+
)
|
137 |
+
self.hparams.train_logger.log_stats(
|
138 |
+
stats_meta={
|
139 |
+
"epoch": epoch,
|
140 |
+
"lr_model": old_lr_model,
|
141 |
+
"lr_wav2vec": old_lr_wav2vec,
|
142 |
+
},
|
143 |
+
train_stats=self.train_stats,
|
144 |
+
valid_stats=stage_stats,
|
145 |
+
)
|
146 |
+
self.checkpointer.save_and_keep_only(
|
147 |
+
meta={"WER": stage_stats["WER"]}, min_keys=["WER"],
|
148 |
+
)
|
149 |
+
elif stage == sb.Stage.TEST:
|
150 |
+
self.hparams.train_logger.log_stats(
|
151 |
+
stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
|
152 |
+
test_stats=stage_stats,
|
153 |
+
)
|
154 |
+
with open(self.hparams.wer_file, "w") as w:
|
155 |
+
self.wer_metric.write_stats(w)
|
156 |
+
|
157 |
+
def init_optimizers(self):
|
158 |
+
"Initializes the wav2vec2 optimizer and model optimizer"
|
159 |
+
self.wav2vec_optimizer = self.hparams.wav2vec_opt_class(
|
160 |
+
self.modules.wav2vec2.parameters()
|
161 |
+
)
|
162 |
+
self.model_optimizer = self.hparams.model_opt_class(
|
163 |
+
self.hparams.model.parameters()
|
164 |
+
)
|
165 |
+
|
166 |
+
if self.checkpointer is not None:
|
167 |
+
self.checkpointer.add_recoverable(
|
168 |
+
"wav2vec_opt", self.wav2vec_optimizer
|
169 |
+
)
|
170 |
+
self.checkpointer.add_recoverable("modelopt", self.model_optimizer)
|
171 |
+
|
172 |
+
|
173 |
+
def dataio_prepare(hparams):
|
174 |
+
"""This function prepares the datasets to be used in the brain class.
|
175 |
+
It also defines the data processing pipeline through user-defined functions."""
|
176 |
+
data_folder = hparams["data_folder"]
|
177 |
+
|
178 |
+
train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
179 |
+
csv_path=hparams["train_csv"], replacements={"data_root": data_folder},
|
180 |
+
)
|
181 |
+
|
182 |
+
if hparams["sorting"] == "ascending":
|
183 |
+
# we sort training data to speed up training and get better results.
|
184 |
+
train_data = train_data.filtered_sorted(sort_key="duration")
|
185 |
+
# when sorting do not shuffle in dataloader ! otherwise is pointless
|
186 |
+
hparams["train_dataloader_opts"]["shuffle"] = False
|
187 |
+
|
188 |
+
elif hparams["sorting"] == "descending":
|
189 |
+
train_data = train_data.filtered_sorted(
|
190 |
+
sort_key="duration", reverse=True
|
191 |
+
)
|
192 |
+
# when sorting do not shuffle in dataloader ! otherwise is pointless
|
193 |
+
hparams["train_dataloader_opts"]["shuffle"] = False
|
194 |
+
|
195 |
+
elif hparams["sorting"] == "random":
|
196 |
+
pass
|
197 |
+
|
198 |
+
else:
|
199 |
+
raise NotImplementedError(
|
200 |
+
"sorting must be random, ascending or descending"
|
201 |
+
)
|
202 |
+
|
203 |
+
valid_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
204 |
+
csv_path=hparams["valid_csv"], replacements={"data_root": data_folder},
|
205 |
+
)
|
206 |
+
valid_data = valid_data.filtered_sorted(sort_key="duration")
|
207 |
+
|
208 |
+
# test is separate
|
209 |
+
test_datasets = {}
|
210 |
+
for csv_file in hparams["test_csv"]:
|
211 |
+
name = Path(csv_file).stem
|
212 |
+
test_datasets[name] = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
213 |
+
csv_path=csv_file, replacements={"data_root": data_folder}
|
214 |
+
)
|
215 |
+
test_datasets[name] = test_datasets[name].filtered_sorted(
|
216 |
+
sort_key="duration"
|
217 |
+
)
|
218 |
+
|
219 |
+
datasets = [train_data, valid_data] + [i for k, i in test_datasets.items()]
|
220 |
+
|
221 |
+
# 2. Define audio pipeline:
|
222 |
+
@sb.utils.data_pipeline.takes("wav", "sr")
|
223 |
+
@sb.utils.data_pipeline.provides("sig")
|
224 |
+
def audio_pipeline(wav, sr):
|
225 |
+
sig = sb.dataio.dataio.read_audio(wav)
|
226 |
+
sig = resamplers[sr](sig)
|
227 |
+
return sig
|
228 |
+
|
229 |
+
sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline)
|
230 |
+
label_encoder = sb.dataio.encoder.CTCTextEncoder()
|
231 |
+
|
232 |
+
# 3. Define text pipeline:
|
233 |
+
@sb.utils.data_pipeline.takes("wrd")
|
234 |
+
@sb.utils.data_pipeline.provides(
|
235 |
+
"wrd", "char_list", "tokens_list", "tokens_bos", "tokens_eos", "tokens"
|
236 |
+
)
|
237 |
+
def text_pipeline(wrd):
|
238 |
+
yield wrd
|
239 |
+
char_list = list(wrd)
|
240 |
+
yield char_list
|
241 |
+
tokens_list = label_encoder.encode_sequence(char_list)
|
242 |
+
yield tokens_list
|
243 |
+
tokens_bos = torch.LongTensor([hparams["bos_index"]] + (tokens_list))
|
244 |
+
yield tokens_bos
|
245 |
+
tokens_eos = torch.LongTensor(tokens_list + [hparams["eos_index"]])
|
246 |
+
yield tokens_eos
|
247 |
+
tokens = torch.LongTensor(tokens_list)
|
248 |
+
yield tokens
|
249 |
+
|
250 |
+
sb.dataio.dataset.add_dynamic_item(datasets, text_pipeline)
|
251 |
+
|
252 |
+
lab_enc_file = os.path.join(hparams["save_folder"], "label_encoder.txt")
|
253 |
+
special_labels = {
|
254 |
+
"bos_label": hparams["bos_index"],
|
255 |
+
"eos_label": hparams["eos_index"],
|
256 |
+
"blank_label": hparams["blank_index"],
|
257 |
+
}
|
258 |
+
label_encoder.load_or_create(
|
259 |
+
path=lab_enc_file,
|
260 |
+
from_didatasets=[train_data],
|
261 |
+
output_key="char_list",
|
262 |
+
special_labels=special_labels,
|
263 |
+
sequence_input=True,
|
264 |
+
)
|
265 |
+
|
266 |
+
# 4. Set output:
|
267 |
+
sb.dataio.dataset.set_output_keys(
|
268 |
+
datasets,
|
269 |
+
["id", "sig", "wrd", "char_list", "tokens_bos", "tokens_eos", "tokens"],
|
270 |
+
)
|
271 |
+
return train_data, valid_data, test_datasets, label_encoder
|
272 |
+
|
273 |
+
|
274 |
+
if __name__ == "__main__":
|
275 |
+
|
276 |
+
# CLI:
|
277 |
+
hparams_file, run_opts, overrides = sb.parse_arguments(sys.argv[1:])
|
278 |
+
|
279 |
+
# If distributed_launch=True then
|
280 |
+
# create ddp_group with the right communication protocol
|
281 |
+
sb.utils.distributed.ddp_init_group(run_opts)
|
282 |
+
|
283 |
+
with open(hparams_file) as fin:
|
284 |
+
hparams = load_hyperpyyaml(fin, overrides)
|
285 |
+
|
286 |
+
# Create experiment directory
|
287 |
+
sb.create_experiment_directory(
|
288 |
+
experiment_directory=hparams["output_folder"],
|
289 |
+
hyperparams_to_save=hparams_file,
|
290 |
+
overrides=overrides,
|
291 |
+
)
|
292 |
+
|
293 |
+
# Dataset prep (parsing Librispeech)
|
294 |
+
|
295 |
+
resampler_8000 = T.Resample(8000, 16000, dtype=torch.float)
|
296 |
+
|
297 |
+
resampler_44100 =T.Resample(44100, 16000, dtype=torch.float)
|
298 |
+
resampler_32000 =T.Resample(32000, 16000, dtype=torch.float)
|
299 |
+
resampler_48000 =T.Resample(48000, 16000, dtype=torch.float)
|
300 |
+
|
301 |
+
|
302 |
+
resamplers = {"48000": resampler_48000,"8000": resampler_8000, "44100":resampler_44100, "32000":resampler_32000}
|
303 |
+
|
304 |
+
# here we create the datasets objects as well as tokenization and encoding
|
305 |
+
train_data, valid_data, test_datasets, label_encoder = dataio_prepare(
|
306 |
+
hparams
|
307 |
+
)
|
308 |
+
|
309 |
+
# Trainer initialization
|
310 |
+
asr_brain = ASR(
|
311 |
+
modules=hparams["modules"],
|
312 |
+
hparams=hparams,
|
313 |
+
run_opts=run_opts,
|
314 |
+
checkpointer=hparams["checkpointer"],
|
315 |
+
)
|
316 |
+
asr_brain.device= "cpu"
|
317 |
+
asr_brain.modules.to("cpu")
|
318 |
+
|
319 |
+
# We dynamicaly add the tokenizer to our brain class.
|
320 |
+
# NB: This tokenizer corresponds to the one used for the LM!!
|
321 |
+
asr_brain.tokenizer = label_encoder
|
322 |
+
|
323 |
+
# Training
|
324 |
+
asr_brain.fit(
|
325 |
+
asr_brain.hparams.epoch_counter,
|
326 |
+
train_data,
|
327 |
+
valid_data,
|
328 |
+
train_loader_kwargs=hparams["train_dataloader_opts"],
|
329 |
+
valid_loader_kwargs=hparams["valid_dataloader_opts"],
|
330 |
+
)
|
331 |
+
|
332 |
+
# Testing
|
333 |
+
for k in test_datasets.keys(): # keys are test_clean, test_other etc
|
334 |
+
asr_brain.hparams.wer_file = os.path.join(
|
335 |
+
hparams["output_folder"], "wer_{}.txt".format(k)
|
336 |
+
)
|
337 |
+
asr_brain.evaluate(
|
338 |
+
test_datasets[k], test_loader_kwargs=hparams["test_dataloader_opts"]
|
339 |
+
)
|
debugging.csv
ADDED
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
ID,duration,wav,sr,wrd
|
2 |
+
Salah1,4.96,samples/Salah1.wav,48000,نحب ماكلة بنينة كسكروت نظيف و رخيص
|
3 |
+
Salah2,3.84,samples/Salah2.wav,48000,باهي وقتاش نمشيو ال تونس
|
4 |
+
Salah3,4.32,samples/Salah3.wav,48000,اعطيني خمسة الاف و خمسة ميا بلاهي
|
5 |
+
Salah4,3.52,samples/Salah4.wav,48000,تعبت هاني راكش في الدار
|
6 |
+
Salah5,3.52,samples/Salah5.wav,48000,نهار السبت ماشي نقرى ان شاء الله
|
7 |
+
Salah6,4.0,samples/Salah6.wav,48000,زعما نلقى أحمد في الستاد ولا ماهوش هوني
|
8 |
+
Salah7,3.84,samples/Salah7.wav,48000,نحب نمشي ال بنزرت نرتاح شوية
|
9 |
+
Salah8,4.16,samples/Salah8.wav,48000,حكيت مع لولاد قالولي كل شي مريقل نهار السبت
|
10 |
+
Salah9,4.8,samples/Salah9.wav,48000,ناكل كفتاجي و نجم نشري شوية حوت زادة
|
11 |
+
Salah10,4.0,samples/Salah10.wav,48000,انتي خويا و عشيري صالح نحبك
|
file.wav
ADDED
Binary file (288 kB). View file
|
|
lm_decoded_ctc.py
ADDED
@@ -0,0 +1,376 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env/python3
|
2 |
+
"""Recipe for training a wav2vec-based ctc ASR system with librispeech.
|
3 |
+
The system employs wav2vec as its encoder. Decoding is performed with
|
4 |
+
ctc greedy decoder.
|
5 |
+
To run this recipe, do the following:
|
6 |
+
> python train_with_wav2vec.py hparams/train_with_wav2vec.yaml
|
7 |
+
The neural network is trained on CTC likelihood target and character units
|
8 |
+
are used as basic recognition tokens. Training is performed on the full
|
9 |
+
LibriSpeech dataset (960 h).
|
10 |
+
|
11 |
+
Authors
|
12 |
+
* Sung-Lin Yeh 2021
|
13 |
+
* Titouan Parcollet 2021
|
14 |
+
* Ju-Chieh Chou 2020
|
15 |
+
* Mirco Ravanelli 2020
|
16 |
+
* Abdel Heba 2020
|
17 |
+
* Peter Plantinga 2020
|
18 |
+
* Samuele Cornell 2020
|
19 |
+
"""
|
20 |
+
|
21 |
+
import os
|
22 |
+
import sys
|
23 |
+
import torch
|
24 |
+
import logging
|
25 |
+
import speechbrain as sb
|
26 |
+
from speechbrain.utils.distributed import run_on_main
|
27 |
+
from hyperpyyaml import load_hyperpyyaml
|
28 |
+
from pathlib import Path
|
29 |
+
from pyctcdecode import build_ctcdecoder
|
30 |
+
|
31 |
+
logger = logging.getLogger(__name__)
|
32 |
+
|
33 |
+
|
34 |
+
# Define training procedure
|
35 |
+
class ASR(sb.Brain):
|
36 |
+
def compute_forward(self, batch, stage):
|
37 |
+
"""Forward computations from the waveform batches to the output probabilities."""
|
38 |
+
batch = batch.to(self.device)
|
39 |
+
wavs, wav_lens = batch.sig
|
40 |
+
tokens_bos, _ = batch.tokens_bos
|
41 |
+
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
|
42 |
+
|
43 |
+
# Forward pass
|
44 |
+
feats = self.modules.wav2vec2(wavs)
|
45 |
+
|
46 |
+
x = self.modules.enc(feats.detach())[0]
|
47 |
+
#x = self.modules.enc(feats.detach())
|
48 |
+
# Compute outputs
|
49 |
+
p_tokens = None
|
50 |
+
logits = self.modules.ctc_lin(x)
|
51 |
+
p_ctc = self.hparams.log_softmax(logits)
|
52 |
+
if stage != sb.Stage.TRAIN:
|
53 |
+
p_tokens = sb.decoders.ctc_greedy_decode(
|
54 |
+
p_ctc, wav_lens, blank_id=self.hparams.blank_index
|
55 |
+
)
|
56 |
+
return p_ctc, wav_lens, p_tokens
|
57 |
+
|
58 |
+
def compute_objectives(self, predictions, batch, stage):
|
59 |
+
"""Computes the loss (CTC+NLL) given predictions and targets."""
|
60 |
+
|
61 |
+
p_ctc, wav_lens, predicted_tokens = predictions
|
62 |
+
|
63 |
+
ids = batch.id
|
64 |
+
tokens_eos, tokens_eos_lens = batch.tokens_eos
|
65 |
+
tokens, tokens_lens = batch.tokens
|
66 |
+
|
67 |
+
if hasattr(self.modules, "env_corrupt") and stage == sb.Stage.TRAIN:
|
68 |
+
tokens_eos = torch.cat([tokens_eos, tokens_eos], dim=0)
|
69 |
+
tokens_eos_lens = torch.cat(
|
70 |
+
[tokens_eos_lens, tokens_eos_lens], dim=0
|
71 |
+
)
|
72 |
+
tokens = torch.cat([tokens, tokens], dim=0)
|
73 |
+
tokens_lens = torch.cat([tokens_lens, tokens_lens], dim=0)
|
74 |
+
|
75 |
+
loss_ctc = self.hparams.ctc_cost(p_ctc, tokens, wav_lens, tokens_lens)
|
76 |
+
loss = loss_ctc
|
77 |
+
|
78 |
+
if stage != sb.Stage.TRAIN:
|
79 |
+
# Decode token terms to words
|
80 |
+
predicted_words = [
|
81 |
+
"".join(self.tokenizer.decode_ndim(utt_seq)).split(" ")
|
82 |
+
for utt_seq in predicted_tokens
|
83 |
+
]
|
84 |
+
predicted_words =[]
|
85 |
+
for logs in p_ctc:
|
86 |
+
text = decoder.decode(logs.detach().cpu().numpy())
|
87 |
+
predicted_words.append(text.split(" "))
|
88 |
+
|
89 |
+
target_words = [wrd.split(" ") for wrd in batch.wrd]
|
90 |
+
self.wer_metric.append(ids, predicted_words, target_words)
|
91 |
+
self.cer_metric.append(ids, predicted_words, target_words)
|
92 |
+
|
93 |
+
return loss
|
94 |
+
|
95 |
+
def fit_batch(self, batch):
|
96 |
+
"""Train the parameters given a single batch in input"""
|
97 |
+
predictions = self.compute_forward(batch, sb.Stage.TRAIN)
|
98 |
+
loss = self.compute_objectives(predictions, batch, sb.Stage.TRAIN)
|
99 |
+
loss.backward()
|
100 |
+
if self.check_gradients(loss):
|
101 |
+
self.wav2vec_optimizer.step()
|
102 |
+
self.model_optimizer.step()
|
103 |
+
|
104 |
+
self.wav2vec_optimizer.zero_grad()
|
105 |
+
self.model_optimizer.zero_grad()
|
106 |
+
|
107 |
+
return loss.detach()
|
108 |
+
|
109 |
+
def evaluate_batch(self, batch, stage):
|
110 |
+
"""Computations needed for validation/test batches"""
|
111 |
+
predictions = self.compute_forward(batch, stage=stage)
|
112 |
+
with torch.no_grad():
|
113 |
+
loss = self.compute_objectives(predictions, batch, stage=stage)
|
114 |
+
return loss.detach()
|
115 |
+
|
116 |
+
def on_stage_start(self, stage, epoch):
|
117 |
+
"""Gets called at the beginning of each epoch"""
|
118 |
+
if stage != sb.Stage.TRAIN:
|
119 |
+
self.cer_metric = self.hparams.cer_computer()
|
120 |
+
self.wer_metric = self.hparams.error_rate_computer()
|
121 |
+
|
122 |
+
def on_stage_end(self, stage, stage_loss, epoch):
|
123 |
+
"""Gets called at the end of an epoch."""
|
124 |
+
# Compute/store important stats
|
125 |
+
stage_stats = {"loss": stage_loss}
|
126 |
+
if stage == sb.Stage.TRAIN:
|
127 |
+
self.train_stats = stage_stats
|
128 |
+
else:
|
129 |
+
stage_stats["CER"] = self.cer_metric.summarize("error_rate")
|
130 |
+
stage_stats["WER"] = self.wer_metric.summarize("error_rate")
|
131 |
+
|
132 |
+
# Perform end-of-iteration things, like annealing, logging, etc.
|
133 |
+
if stage == sb.Stage.VALID:
|
134 |
+
old_lr_model, new_lr_model = self.hparams.lr_annealing_model(
|
135 |
+
stage_stats["loss"]
|
136 |
+
)
|
137 |
+
old_lr_wav2vec, new_lr_wav2vec = self.hparams.lr_annealing_wav2vec(
|
138 |
+
stage_stats["loss"]
|
139 |
+
)
|
140 |
+
sb.nnet.schedulers.update_learning_rate(
|
141 |
+
self.model_optimizer, new_lr_model
|
142 |
+
)
|
143 |
+
sb.nnet.schedulers.update_learning_rate(
|
144 |
+
self.wav2vec_optimizer, new_lr_wav2vec
|
145 |
+
)
|
146 |
+
self.hparams.train_logger.log_stats(
|
147 |
+
stats_meta={
|
148 |
+
"epoch": epoch,
|
149 |
+
"lr_model": old_lr_model,
|
150 |
+
"lr_wav2vec": old_lr_wav2vec,
|
151 |
+
},
|
152 |
+
train_stats=self.train_stats,
|
153 |
+
valid_stats=stage_stats,
|
154 |
+
)
|
155 |
+
self.checkpointer.save_and_keep_only(
|
156 |
+
meta={"WER": stage_stats["WER"]}, min_keys=["WER"],
|
157 |
+
)
|
158 |
+
elif stage == sb.Stage.TEST:
|
159 |
+
self.hparams.train_logger.log_stats(
|
160 |
+
stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
|
161 |
+
test_stats=stage_stats,
|
162 |
+
)
|
163 |
+
with open(self.hparams.wer_file, "w") as w:
|
164 |
+
self.wer_metric.write_stats(w)
|
165 |
+
|
166 |
+
def init_optimizers(self):
|
167 |
+
"Initializes the wav2vec2 optimizer and model optimizer"
|
168 |
+
self.wav2vec_optimizer = self.hparams.wav2vec_opt_class(
|
169 |
+
self.modules.wav2vec2.parameters()
|
170 |
+
)
|
171 |
+
self.model_optimizer = self.hparams.model_opt_class(
|
172 |
+
self.hparams.model.parameters()
|
173 |
+
)
|
174 |
+
|
175 |
+
if self.checkpointer is not None:
|
176 |
+
self.checkpointer.add_recoverable(
|
177 |
+
"wav2vec_opt", self.wav2vec_optimizer
|
178 |
+
)
|
179 |
+
self.checkpointer.add_recoverable("modelopt", self.model_optimizer)
|
180 |
+
|
181 |
+
|
182 |
+
def dataio_prepare(hparams):
|
183 |
+
"""This function prepares the datasets to be used in the brain class.
|
184 |
+
It also defines the data processing pipeline through user-defined functions."""
|
185 |
+
data_folder = hparams["data_folder"]
|
186 |
+
|
187 |
+
train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
188 |
+
csv_path=hparams["train_csv"], replacements={"data_root": data_folder},
|
189 |
+
)
|
190 |
+
|
191 |
+
if hparams["sorting"] == "ascending":
|
192 |
+
# we sort training data to speed up training and get better results.
|
193 |
+
train_data = train_data.filtered_sorted(sort_key="duration")
|
194 |
+
# when sorting do not shuffle in dataloader ! otherwise is pointless
|
195 |
+
hparams["train_dataloader_opts"]["shuffle"] = False
|
196 |
+
|
197 |
+
elif hparams["sorting"] == "descending":
|
198 |
+
train_data = train_data.filtered_sorted(
|
199 |
+
sort_key="duration", reverse=True
|
200 |
+
)
|
201 |
+
# when sorting do not shuffle in dataloader ! otherwise is pointless
|
202 |
+
hparams["train_dataloader_opts"]["shuffle"] = False
|
203 |
+
|
204 |
+
elif hparams["sorting"] == "random":
|
205 |
+
pass
|
206 |
+
|
207 |
+
else:
|
208 |
+
raise NotImplementedError(
|
209 |
+
"sorting must be random, ascending or descending"
|
210 |
+
)
|
211 |
+
|
212 |
+
valid_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
213 |
+
csv_path=hparams["valid_csv"], replacements={"data_root": data_folder},
|
214 |
+
)
|
215 |
+
valid_data = valid_data.filtered_sorted(sort_key="duration")
|
216 |
+
|
217 |
+
# test is separate
|
218 |
+
test_datasets = {}
|
219 |
+
for csv_file in hparams["test_csv"]:
|
220 |
+
name = Path(csv_file).stem
|
221 |
+
test_datasets[name] = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
222 |
+
csv_path=csv_file, replacements={"data_root": data_folder}
|
223 |
+
)
|
224 |
+
test_datasets[name] = test_datasets[name].filtered_sorted(
|
225 |
+
sort_key="duration"
|
226 |
+
)
|
227 |
+
|
228 |
+
datasets = [train_data, valid_data] + [i for k, i in test_datasets.items()]
|
229 |
+
|
230 |
+
# 2. Define audio pipeline:
|
231 |
+
@sb.utils.data_pipeline.takes("wav")
|
232 |
+
@sb.utils.data_pipeline.provides("sig")
|
233 |
+
def audio_pipeline(wav):
|
234 |
+
sig = sb.dataio.dataio.read_audio(wav)
|
235 |
+
return sig
|
236 |
+
|
237 |
+
sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline)
|
238 |
+
label_encoder = sb.dataio.encoder.CTCTextEncoder()
|
239 |
+
|
240 |
+
# 3. Define text pipeline:
|
241 |
+
@sb.utils.data_pipeline.takes("wrd")
|
242 |
+
@sb.utils.data_pipeline.provides(
|
243 |
+
"wrd", "char_list", "tokens_list", "tokens_bos", "tokens_eos", "tokens"
|
244 |
+
)
|
245 |
+
def text_pipeline(wrd):
|
246 |
+
yield wrd
|
247 |
+
char_list = list(wrd)
|
248 |
+
yield char_list
|
249 |
+
tokens_list = label_encoder.encode_sequence(char_list)
|
250 |
+
yield tokens_list
|
251 |
+
tokens_bos = torch.LongTensor([hparams["bos_index"]] + (tokens_list))
|
252 |
+
yield tokens_bos
|
253 |
+
tokens_eos = torch.LongTensor(tokens_list + [hparams["eos_index"]])
|
254 |
+
yield tokens_eos
|
255 |
+
tokens = torch.LongTensor(tokens_list)
|
256 |
+
yield tokens
|
257 |
+
|
258 |
+
sb.dataio.dataset.add_dynamic_item(datasets, text_pipeline)
|
259 |
+
|
260 |
+
lab_enc_file = os.path.join(hparams["save_folder"], "label_encoder.txt")
|
261 |
+
special_labels = {
|
262 |
+
"bos_label": hparams["bos_index"],
|
263 |
+
"eos_label": hparams["eos_index"],
|
264 |
+
"blank_label": hparams["blank_index"],
|
265 |
+
}
|
266 |
+
label_encoder.load_or_create(
|
267 |
+
path=lab_enc_file,
|
268 |
+
from_didatasets=[train_data],
|
269 |
+
output_key="char_list",
|
270 |
+
special_labels=special_labels,
|
271 |
+
sequence_input=True,
|
272 |
+
)
|
273 |
+
|
274 |
+
# 4. Set output:
|
275 |
+
sb.dataio.dataset.set_output_keys(
|
276 |
+
datasets,
|
277 |
+
["id", "sig", "wrd", "char_list", "tokens_bos", "tokens_eos", "tokens"],
|
278 |
+
)
|
279 |
+
return train_data, valid_data, test_datasets, label_encoder
|
280 |
+
|
281 |
+
|
282 |
+
if __name__ == "__main__":
|
283 |
+
|
284 |
+
# CLI:
|
285 |
+
hparams_file, run_opts, overrides = sb.parse_arguments(sys.argv[1:])
|
286 |
+
|
287 |
+
# If distributed_launch=True then
|
288 |
+
# create ddp_group with the right communication protocol
|
289 |
+
sb.utils.distributed.ddp_init_group(run_opts)
|
290 |
+
|
291 |
+
with open(hparams_file) as fin:
|
292 |
+
hparams = load_hyperpyyaml(fin, overrides)
|
293 |
+
|
294 |
+
# Create experiment directory
|
295 |
+
sb.create_experiment_directory(
|
296 |
+
experiment_directory=hparams["output_folder"],
|
297 |
+
hyperparams_to_save=hparams_file,
|
298 |
+
overrides=overrides,
|
299 |
+
)
|
300 |
+
def read_labels_file(labels_file):
|
301 |
+
with open(labels_file, "r") as lf:
|
302 |
+
lines = lf.read().splitlines()
|
303 |
+
division = "==="
|
304 |
+
numbers = {}
|
305 |
+
for line in lines :
|
306 |
+
if division in line :
|
307 |
+
break
|
308 |
+
string, number = line.split("=>")
|
309 |
+
number = int(number)
|
310 |
+
string = string[1:-2]
|
311 |
+
numbers[number] = string
|
312 |
+
return [numbers[x] for x in range(len(numbers))]
|
313 |
+
labels = read_labels_file(os.path.join(hparams["save_folder"], "label_encoder.txt"))
|
314 |
+
print(labels)
|
315 |
+
labels = [""] + labels[1:]
|
316 |
+
print(len(labels))
|
317 |
+
decoder = build_ctcdecoder(
|
318 |
+
labels,
|
319 |
+
kenlm_model_path="/gpfsstore/rech/nou/uzn19yk/4-gram.arpa", # either .arpa or .bin file
|
320 |
+
alpha=0.5, # tuned on a val set
|
321 |
+
beta=1.0, # tuned on a val set
|
322 |
+
)
|
323 |
+
|
324 |
+
# Dataset prep (parsing Librispeech)
|
325 |
+
|
326 |
+
# multi-gpu (ddp) save data preparation
|
327 |
+
"""
|
328 |
+
run_on_main(
|
329 |
+
prepare_librispeech,
|
330 |
+
kwargs={
|
331 |
+
"data_folder": hparams["data_folder"],
|
332 |
+
"tr_splits": hparams["train_splits"],
|
333 |
+
"dev_splits": hparams["dev_splits"],
|
334 |
+
"te_splits": hparams["test_splits"],
|
335 |
+
"save_folder": hparams["output_folder"],
|
336 |
+
"merge_lst": hparams["train_splits"],
|
337 |
+
"merge_name": "train.csv",
|
338 |
+
"skip_prep": hparams["skip_prep"],
|
339 |
+
},
|
340 |
+
)
|
341 |
+
"""
|
342 |
+
|
343 |
+
# here we create the datasets objects as well as tokenization and encoding
|
344 |
+
train_data, valid_data, test_datasets, label_encoder = dataio_prepare(
|
345 |
+
hparams
|
346 |
+
)
|
347 |
+
|
348 |
+
# Trainer initialization
|
349 |
+
asr_brain = ASR(
|
350 |
+
modules=hparams["modules"],
|
351 |
+
hparams=hparams,
|
352 |
+
run_opts=run_opts,
|
353 |
+
checkpointer=hparams["checkpointer"],
|
354 |
+
)
|
355 |
+
|
356 |
+
# We dynamicaly add the tokenizer to our brain class.
|
357 |
+
# NB: This tokenizer corresponds to the one used for the LM!!
|
358 |
+
asr_brain.tokenizer = label_encoder
|
359 |
+
|
360 |
+
# Training
|
361 |
+
asr_brain.fit(
|
362 |
+
asr_brain.hparams.epoch_counter,
|
363 |
+
train_data,
|
364 |
+
valid_data,
|
365 |
+
train_loader_kwargs=hparams["train_dataloader_opts"],
|
366 |
+
valid_loader_kwargs=hparams["valid_dataloader_opts"],
|
367 |
+
)
|
368 |
+
|
369 |
+
# Testing
|
370 |
+
for k in test_datasets.keys(): # keys are test_clean, test_other etc
|
371 |
+
asr_brain.hparams.wer_file = os.path.join(
|
372 |
+
hparams["output_folder"], "wer_{}.txt".format(k)
|
373 |
+
)
|
374 |
+
asr_brain.evaluate(
|
375 |
+
test_datasets[k], test_loader_kwargs=hparams["test_dataloader_opts"]
|
376 |
+
)
|
lm_tunisian.py
ADDED
@@ -0,0 +1,361 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env/python3
|
2 |
+
"""Recipe for training a wav2vec-based ctc ASR system with librispeech.
|
3 |
+
The system employs wav2vec as its encoder. Decoding is performed with
|
4 |
+
ctc greedy decoder.
|
5 |
+
To run this recipe, do the following:
|
6 |
+
> python train_with_wav2vec.py hparams/train_with_wav2vec.yaml
|
7 |
+
The neural network is trained on CTC likelihood target and character units
|
8 |
+
are used as basic recognition tokens. Training is performed on the full
|
9 |
+
LibriSpeech dataset (960 h).
|
10 |
+
|
11 |
+
Authors
|
12 |
+
* Sung-Lin Yeh 2021
|
13 |
+
* Titouan Parcollet 2021
|
14 |
+
* Ju-Chieh Chou 2020
|
15 |
+
* Mirco Ravanelli 2020
|
16 |
+
* Abdel Heba 2020
|
17 |
+
* Peter Plantinga 2020
|
18 |
+
* Samuele Cornell 2020
|
19 |
+
"""
|
20 |
+
|
21 |
+
import os
|
22 |
+
import sys
|
23 |
+
import torch
|
24 |
+
import logging
|
25 |
+
import speechbrain as sb
|
26 |
+
from speechbrain.utils.distributed import run_on_main
|
27 |
+
from hyperpyyaml import load_hyperpyyaml
|
28 |
+
from pathlib import Path
|
29 |
+
import torchaudio.transforms as T
|
30 |
+
|
31 |
+
from pyctcdecode import build_ctcdecoder
|
32 |
+
logger = logging.getLogger(__name__)
|
33 |
+
|
34 |
+
# Define training procedure
|
35 |
+
class ASR(sb.Brain):
|
36 |
+
def compute_forward(self, batch, stage):
|
37 |
+
"""Forward computations from the waveform batches to the output probabilities."""
|
38 |
+
batch = batch.to(self.device)
|
39 |
+
wavs, wav_lens = batch.sig
|
40 |
+
tokens_bos, _ = batch.tokens_bos
|
41 |
+
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
|
42 |
+
|
43 |
+
# Forward pass
|
44 |
+
feats = self.modules.wav2vec2(wavs)
|
45 |
+
x = self.modules.enc(feats)
|
46 |
+
# Compute outputs
|
47 |
+
p_tokens = None
|
48 |
+
logits = self.modules.ctc_lin(x)
|
49 |
+
p_ctc = self.hparams.log_softmax(logits)
|
50 |
+
if stage != sb.Stage.TRAIN:
|
51 |
+
p_tokens = sb.decoders.ctc_greedy_decode(
|
52 |
+
p_ctc, wav_lens, blank_id=self.hparams.blank_index
|
53 |
+
)
|
54 |
+
return p_ctc, wav_lens, p_tokens
|
55 |
+
|
56 |
+
def compute_objectives(self, predictions, batch, stage):
|
57 |
+
"""Computes the loss (CTC+NLL) given predictions and targets."""
|
58 |
+
|
59 |
+
p_ctc, wav_lens, predicted_tokens = predictions
|
60 |
+
|
61 |
+
ids = batch.id
|
62 |
+
tokens_eos, tokens_eos_lens = batch.tokens_eos
|
63 |
+
tokens, tokens_lens = batch.tokens
|
64 |
+
|
65 |
+
if hasattr(self.modules, "env_corrupt") and stage == sb.Stage.TRAIN:
|
66 |
+
tokens_eos = torch.cat([tokens_eos, tokens_eos], dim=0)
|
67 |
+
tokens_eos_lens = torch.cat(
|
68 |
+
[tokens_eos_lens, tokens_eos_lens], dim=0
|
69 |
+
)
|
70 |
+
tokens = torch.cat([tokens, tokens], dim=0)
|
71 |
+
tokens_lens = torch.cat([tokens_lens, tokens_lens], dim=0)
|
72 |
+
|
73 |
+
loss_ctc = self.hparams.ctc_cost(p_ctc, tokens, wav_lens, tokens_lens)
|
74 |
+
loss = loss_ctc
|
75 |
+
if stage != sb.Stage.TRAIN:
|
76 |
+
# Decode token terms to words
|
77 |
+
predicted_words =[]
|
78 |
+
for logs in p_ctc:
|
79 |
+
text = decoder.decode(logs.detach().cpu().numpy())
|
80 |
+
predicted_words.append(text.split(" "))
|
81 |
+
|
82 |
+
|
83 |
+
target_words = [wrd.split(" ") for wrd in batch.wrd]
|
84 |
+
self.wer_metric.append(ids, predicted_words, target_words)
|
85 |
+
self.cer_metric.append(ids, predicted_words, target_words)
|
86 |
+
|
87 |
+
return loss
|
88 |
+
|
89 |
+
def fit_batch(self, batch):
|
90 |
+
"""Train the parameters given a single batch in input"""
|
91 |
+
predictions = self.compute_forward(batch, sb.Stage.TRAIN)
|
92 |
+
loss = self.compute_objectives(predictions, batch, sb.Stage.TRAIN)
|
93 |
+
loss.backward()
|
94 |
+
if self.check_gradients(loss):
|
95 |
+
self.wav2vec_optimizer.step()
|
96 |
+
self.model_optimizer.step()
|
97 |
+
|
98 |
+
self.wav2vec_optimizer.zero_grad()
|
99 |
+
self.model_optimizer.zero_grad()
|
100 |
+
|
101 |
+
return loss.detach()
|
102 |
+
|
103 |
+
def evaluate_batch(self, batch, stage):
|
104 |
+
"""Computations needed for validation/test batches"""
|
105 |
+
predictions = self.compute_forward(batch, stage=stage)
|
106 |
+
with torch.no_grad():
|
107 |
+
loss = self.compute_objectives(predictions, batch, stage=stage)
|
108 |
+
return loss.detach()
|
109 |
+
|
110 |
+
def on_stage_start(self, stage, epoch):
|
111 |
+
"""Gets called at the beginning of each epoch"""
|
112 |
+
if stage != sb.Stage.TRAIN:
|
113 |
+
self.cer_metric = self.hparams.cer_computer()
|
114 |
+
self.wer_metric = self.hparams.error_rate_computer()
|
115 |
+
|
116 |
+
def on_stage_end(self, stage, stage_loss, epoch):
|
117 |
+
"""Gets called at the end of an epoch."""
|
118 |
+
# Compute/store important stats
|
119 |
+
stage_stats = {"loss": stage_loss}
|
120 |
+
if stage == sb.Stage.TRAIN:
|
121 |
+
self.train_stats = stage_stats
|
122 |
+
else:
|
123 |
+
stage_stats["CER"] = self.cer_metric.summarize("error_rate")
|
124 |
+
stage_stats["WER"] = self.wer_metric.summarize("error_rate")
|
125 |
+
|
126 |
+
# Perform end-of-iteration things, like annealing, logging, etc.
|
127 |
+
if stage == sb.Stage.VALID:
|
128 |
+
old_lr_model, new_lr_model = self.hparams.lr_annealing_model(
|
129 |
+
stage_stats["loss"]
|
130 |
+
)
|
131 |
+
old_lr_wav2vec, new_lr_wav2vec = self.hparams.lr_annealing_wav2vec(
|
132 |
+
stage_stats["loss"]
|
133 |
+
)
|
134 |
+
sb.nnet.schedulers.update_learning_rate(
|
135 |
+
self.model_optimizer, new_lr_model
|
136 |
+
)
|
137 |
+
sb.nnet.schedulers.update_learning_rate(
|
138 |
+
self.wav2vec_optimizer, new_lr_wav2vec
|
139 |
+
)
|
140 |
+
self.hparams.train_logger.log_stats(
|
141 |
+
stats_meta={
|
142 |
+
"epoch": epoch,
|
143 |
+
"lr_model": old_lr_model,
|
144 |
+
"lr_wav2vec": old_lr_wav2vec,
|
145 |
+
},
|
146 |
+
train_stats=self.train_stats,
|
147 |
+
valid_stats=stage_stats,
|
148 |
+
)
|
149 |
+
self.checkpointer.save_and_keep_only(
|
150 |
+
meta={"WER": stage_stats["WER"]}, min_keys=["WER"],
|
151 |
+
)
|
152 |
+
elif stage == sb.Stage.TEST:
|
153 |
+
self.hparams.train_logger.log_stats(
|
154 |
+
stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
|
155 |
+
test_stats=stage_stats,
|
156 |
+
)
|
157 |
+
with open(self.hparams.wer_file, "w") as w:
|
158 |
+
self.wer_metric.write_stats(w)
|
159 |
+
|
160 |
+
def init_optimizers(self):
|
161 |
+
"Initializes the wav2vec2 optimizer and model optimizer"
|
162 |
+
self.wav2vec_optimizer = self.hparams.wav2vec_opt_class(
|
163 |
+
self.modules.wav2vec2.parameters()
|
164 |
+
)
|
165 |
+
self.model_optimizer = self.hparams.model_opt_class(
|
166 |
+
self.hparams.model.parameters()
|
167 |
+
)
|
168 |
+
|
169 |
+
if self.checkpointer is not None:
|
170 |
+
self.checkpointer.add_recoverable(
|
171 |
+
"wav2vec_opt", self.wav2vec_optimizer
|
172 |
+
)
|
173 |
+
self.checkpointer.add_recoverable("modelopt", self.model_optimizer)
|
174 |
+
|
175 |
+
|
176 |
+
def dataio_prepare(hparams):
|
177 |
+
"""This function prepares the datasets to be used in the brain class.
|
178 |
+
It also defines the data processing pipeline through user-defined functions."""
|
179 |
+
data_folder = hparams["data_folder"]
|
180 |
+
|
181 |
+
train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
182 |
+
csv_path=hparams["train_csv"], replacements={"data_root": data_folder},
|
183 |
+
)
|
184 |
+
|
185 |
+
if hparams["sorting"] == "ascending":
|
186 |
+
# we sort training data to speed up training and get better results.
|
187 |
+
train_data = train_data.filtered_sorted(sort_key="duration")
|
188 |
+
# when sorting do not shuffle in dataloader ! otherwise is pointless
|
189 |
+
hparams["train_dataloader_opts"]["shuffle"] = False
|
190 |
+
|
191 |
+
elif hparams["sorting"] == "descending":
|
192 |
+
train_data = train_data.filtered_sorted(
|
193 |
+
sort_key="duration", reverse=True
|
194 |
+
)
|
195 |
+
# when sorting do not shuffle in dataloader ! otherwise is pointless
|
196 |
+
hparams["train_dataloader_opts"]["shuffle"] = False
|
197 |
+
|
198 |
+
elif hparams["sorting"] == "random":
|
199 |
+
pass
|
200 |
+
|
201 |
+
else:
|
202 |
+
raise NotImplementedError(
|
203 |
+
"sorting must be random, ascending or descending"
|
204 |
+
)
|
205 |
+
|
206 |
+
valid_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
207 |
+
csv_path=hparams["valid_csv"], replacements={"data_root": data_folder},
|
208 |
+
)
|
209 |
+
valid_data = valid_data.filtered_sorted(sort_key="duration")
|
210 |
+
|
211 |
+
# test is separate
|
212 |
+
test_datasets = {}
|
213 |
+
for csv_file in hparams["test_csv"]:
|
214 |
+
name = Path(csv_file).stem
|
215 |
+
test_datasets[name] = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
216 |
+
csv_path=csv_file, replacements={"data_root": data_folder}
|
217 |
+
)
|
218 |
+
test_datasets[name] = test_datasets[name].filtered_sorted(
|
219 |
+
sort_key="duration"
|
220 |
+
)
|
221 |
+
|
222 |
+
datasets = [train_data, valid_data] + [i for k, i in test_datasets.items()]
|
223 |
+
|
224 |
+
# 2. Define audio pipeline:
|
225 |
+
@sb.utils.data_pipeline.takes("wav", "sr")
|
226 |
+
@sb.utils.data_pipeline.provides("sig")
|
227 |
+
def audio_pipeline(wav, sr):
|
228 |
+
sig = sb.dataio.dataio.read_audio(wav)
|
229 |
+
sig = resamplers[sr](sig)
|
230 |
+
return sig
|
231 |
+
|
232 |
+
sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline)
|
233 |
+
label_encoder = sb.dataio.encoder.CTCTextEncoder()
|
234 |
+
|
235 |
+
# 3. Define text pipeline:
|
236 |
+
@sb.utils.data_pipeline.takes("wrd")
|
237 |
+
@sb.utils.data_pipeline.provides(
|
238 |
+
"wrd", "char_list", "tokens_list", "tokens_bos", "tokens_eos", "tokens"
|
239 |
+
)
|
240 |
+
def text_pipeline(wrd):
|
241 |
+
yield wrd
|
242 |
+
char_list = list(wrd)
|
243 |
+
yield char_list
|
244 |
+
tokens_list = label_encoder.encode_sequence(char_list)
|
245 |
+
yield tokens_list
|
246 |
+
tokens_bos = torch.LongTensor([hparams["bos_index"]] + (tokens_list))
|
247 |
+
yield tokens_bos
|
248 |
+
tokens_eos = torch.LongTensor(tokens_list + [hparams["eos_index"]])
|
249 |
+
yield tokens_eos
|
250 |
+
tokens = torch.LongTensor(tokens_list)
|
251 |
+
yield tokens
|
252 |
+
|
253 |
+
sb.dataio.dataset.add_dynamic_item(datasets, text_pipeline)
|
254 |
+
|
255 |
+
lab_enc_file = os.path.join(hparams["save_folder"], "label_encoder.txt")
|
256 |
+
special_labels = {
|
257 |
+
"bos_label": hparams["bos_index"],
|
258 |
+
"eos_label": hparams["eos_index"],
|
259 |
+
"blank_label": hparams["blank_index"],
|
260 |
+
}
|
261 |
+
label_encoder.load_or_create(
|
262 |
+
path=lab_enc_file,
|
263 |
+
from_didatasets=[train_data],
|
264 |
+
output_key="char_list",
|
265 |
+
special_labels=special_labels,
|
266 |
+
sequence_input=True,
|
267 |
+
)
|
268 |
+
|
269 |
+
# 4. Set output:
|
270 |
+
sb.dataio.dataset.set_output_keys(
|
271 |
+
datasets,
|
272 |
+
["id", "sig", "wrd", "char_list", "tokens_bos", "tokens_eos", "tokens"],
|
273 |
+
)
|
274 |
+
return train_data, valid_data, test_datasets, label_encoder
|
275 |
+
|
276 |
+
|
277 |
+
if __name__ == "__main__":
|
278 |
+
|
279 |
+
# CLI:
|
280 |
+
hparams_file, run_opts, overrides = sb.parse_arguments(sys.argv[1:])
|
281 |
+
|
282 |
+
# If distributed_launch=True then
|
283 |
+
# create ddp_group with the right communication protocol
|
284 |
+
sb.utils.distributed.ddp_init_group(run_opts)
|
285 |
+
|
286 |
+
with open(hparams_file) as fin:
|
287 |
+
hparams = load_hyperpyyaml(fin, overrides)
|
288 |
+
|
289 |
+
# Create experiment directory
|
290 |
+
sb.create_experiment_directory(
|
291 |
+
experiment_directory=hparams["output_folder"],
|
292 |
+
hyperparams_to_save=hparams_file,
|
293 |
+
overrides=overrides,
|
294 |
+
)
|
295 |
+
def read_labels_file(labels_file):
|
296 |
+
with open(labels_file, "r") as lf:
|
297 |
+
lines = lf.read().splitlines()
|
298 |
+
division = "==="
|
299 |
+
numbers = {}
|
300 |
+
for line in lines :
|
301 |
+
if division in line :
|
302 |
+
break
|
303 |
+
string, number = line.split("=>")
|
304 |
+
number = int(number)
|
305 |
+
string = string[1:-2]
|
306 |
+
numbers[number] = string
|
307 |
+
return [numbers[x] for x in range(len(numbers))]
|
308 |
+
labels = read_labels_file(os.path.join(hparams["save_folder"], "label_encoder.txt"))
|
309 |
+
print(labels)
|
310 |
+
labels = [""] + labels[1:]
|
311 |
+
print(len(labels))
|
312 |
+
decoder = build_ctcdecoder(
|
313 |
+
labels,
|
314 |
+
kenlm_model_path="tunisian.arpa", # either .arpa or .bin file
|
315 |
+
alpha=0.5, # tuned on a val set
|
316 |
+
beta=1.0, # tuned on a val set
|
317 |
+
)
|
318 |
+
|
319 |
+
# Dataset prep (parsing Librispeech)
|
320 |
+
|
321 |
+
resampler_8000 = T.Resample(8000, 16000, dtype=torch.float)
|
322 |
+
|
323 |
+
resampler_44100 =T.Resample(44100, 16000, dtype=torch.float)
|
324 |
+
resampler_48000 =T.Resample(48000, 16000, dtype=torch.float)
|
325 |
+
resamplers = {"8000": resampler_8000, "44100":resampler_44100, "48000": resampler_48000}
|
326 |
+
|
327 |
+
# here we create the datasets objects as well as tokenization and encoding
|
328 |
+
train_data, valid_data, test_datasets, label_encoder = dataio_prepare(
|
329 |
+
hparams
|
330 |
+
)
|
331 |
+
|
332 |
+
# Trainer initialization
|
333 |
+
asr_brain = ASR(
|
334 |
+
modules=hparams["modules"],
|
335 |
+
hparams=hparams,
|
336 |
+
run_opts=run_opts,
|
337 |
+
checkpointer=hparams["checkpointer"],
|
338 |
+
)
|
339 |
+
asr_brain.device= "cpu"
|
340 |
+
asr_brain.modules.to("cpu")
|
341 |
+
# We dynamicaly add the tokenizer to our brain class.
|
342 |
+
# NB: This tokenizer corresponds to the one used for the LM!!
|
343 |
+
asr_brain.tokenizer = label_encoder
|
344 |
+
|
345 |
+
# Training
|
346 |
+
asr_brain.fit(
|
347 |
+
asr_brain.hparams.epoch_counter,
|
348 |
+
train_data,
|
349 |
+
valid_data,
|
350 |
+
train_loader_kwargs=hparams["train_dataloader_opts"],
|
351 |
+
valid_loader_kwargs=hparams["valid_dataloader_opts"],
|
352 |
+
)
|
353 |
+
|
354 |
+
# Testing
|
355 |
+
for k in test_datasets.keys(): # keys are test_clean, test_other etc
|
356 |
+
asr_brain.hparams.wer_file = os.path.join(
|
357 |
+
hparams["output_folder"], "wer_{}.txt".format(k)
|
358 |
+
)
|
359 |
+
asr_brain.evaluate(
|
360 |
+
test_datasets[k], test_loader_kwargs=hparams["test_dataloader_opts"]
|
361 |
+
)
|
partly_frozen_splitted_wavlm/1986/ctc_train.py
ADDED
@@ -0,0 +1,339 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env/python3
|
2 |
+
"""Recipe for training a wav2vec-based ctc ASR system with librispeech.
|
3 |
+
The system employs wav2vec as its encoder. Decoding is performed with
|
4 |
+
ctc greedy decoder.
|
5 |
+
To run this recipe, do the following:
|
6 |
+
> python train_with_wav2vec.py hparams/train_with_wav2vec.yaml
|
7 |
+
The neural network is trained on CTC likelihood target and character units
|
8 |
+
are used as basic recognition tokens. Training is performed on the full
|
9 |
+
LibriSpeech dataset (960 h).
|
10 |
+
|
11 |
+
Authors
|
12 |
+
* Sung-Lin Yeh 2021
|
13 |
+
* Titouan Parcollet 2021
|
14 |
+
* Ju-Chieh Chou 2020
|
15 |
+
* Mirco Ravanelli 2020
|
16 |
+
* Abdel Heba 2020
|
17 |
+
* Peter Plantinga 2020
|
18 |
+
* Samuele Cornell 2020
|
19 |
+
"""
|
20 |
+
|
21 |
+
import os
|
22 |
+
import sys
|
23 |
+
import torch
|
24 |
+
import logging
|
25 |
+
import speechbrain as sb
|
26 |
+
from speechbrain.utils.distributed import run_on_main
|
27 |
+
from hyperpyyaml import load_hyperpyyaml
|
28 |
+
from pathlib import Path
|
29 |
+
import torchaudio.transforms as T
|
30 |
+
logger = logging.getLogger(__name__)
|
31 |
+
|
32 |
+
# Define training procedure
|
33 |
+
class ASR(sb.Brain):
|
34 |
+
def compute_forward(self, batch, stage):
|
35 |
+
"""Forward computations from the waveform batches to the output probabilities."""
|
36 |
+
batch = batch.to(self.device)
|
37 |
+
wavs, wav_lens = batch.sig
|
38 |
+
tokens_bos, _ = batch.tokens_bos
|
39 |
+
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
|
40 |
+
|
41 |
+
# Forward pass
|
42 |
+
feats = self.modules.wav2vec2(wavs)
|
43 |
+
x = self.modules.enc(feats)
|
44 |
+
# Compute outputs
|
45 |
+
p_tokens = None
|
46 |
+
logits = self.modules.ctc_lin(x)
|
47 |
+
p_ctc = self.hparams.log_softmax(logits)
|
48 |
+
if stage != sb.Stage.TRAIN:
|
49 |
+
p_tokens = sb.decoders.ctc_greedy_decode(
|
50 |
+
p_ctc, wav_lens, blank_id=self.hparams.blank_index
|
51 |
+
)
|
52 |
+
return p_ctc, wav_lens, p_tokens
|
53 |
+
|
54 |
+
def compute_objectives(self, predictions, batch, stage):
|
55 |
+
"""Computes the loss (CTC+NLL) given predictions and targets."""
|
56 |
+
|
57 |
+
p_ctc, wav_lens, predicted_tokens = predictions
|
58 |
+
|
59 |
+
ids = batch.id
|
60 |
+
tokens_eos, tokens_eos_lens = batch.tokens_eos
|
61 |
+
tokens, tokens_lens = batch.tokens
|
62 |
+
|
63 |
+
if hasattr(self.modules, "env_corrupt") and stage == sb.Stage.TRAIN:
|
64 |
+
tokens_eos = torch.cat([tokens_eos, tokens_eos], dim=0)
|
65 |
+
tokens_eos_lens = torch.cat(
|
66 |
+
[tokens_eos_lens, tokens_eos_lens], dim=0
|
67 |
+
)
|
68 |
+
tokens = torch.cat([tokens, tokens], dim=0)
|
69 |
+
tokens_lens = torch.cat([tokens_lens, tokens_lens], dim=0)
|
70 |
+
|
71 |
+
loss_ctc = self.hparams.ctc_cost(p_ctc, tokens, wav_lens, tokens_lens)
|
72 |
+
loss = loss_ctc
|
73 |
+
|
74 |
+
if stage != sb.Stage.TRAIN:
|
75 |
+
# Decode token terms to words
|
76 |
+
predicted_words = [
|
77 |
+
"".join(self.tokenizer.decode_ndim(utt_seq)).split(" ")
|
78 |
+
for utt_seq in predicted_tokens
|
79 |
+
]
|
80 |
+
target_words = [wrd.split(" ") for wrd in batch.wrd]
|
81 |
+
self.wer_metric.append(ids, predicted_words, target_words)
|
82 |
+
self.cer_metric.append(ids, predicted_words, target_words)
|
83 |
+
|
84 |
+
return loss
|
85 |
+
|
86 |
+
def fit_batch(self, batch):
|
87 |
+
"""Train the parameters given a single batch in input"""
|
88 |
+
predictions = self.compute_forward(batch, sb.Stage.TRAIN)
|
89 |
+
loss = self.compute_objectives(predictions, batch, sb.Stage.TRAIN)
|
90 |
+
loss.backward()
|
91 |
+
if self.check_gradients(loss):
|
92 |
+
self.wav2vec_optimizer.step()
|
93 |
+
self.model_optimizer.step()
|
94 |
+
|
95 |
+
self.wav2vec_optimizer.zero_grad()
|
96 |
+
self.model_optimizer.zero_grad()
|
97 |
+
|
98 |
+
return loss.detach()
|
99 |
+
|
100 |
+
def evaluate_batch(self, batch, stage):
|
101 |
+
"""Computations needed for validation/test batches"""
|
102 |
+
predictions = self.compute_forward(batch, stage=stage)
|
103 |
+
with torch.no_grad():
|
104 |
+
loss = self.compute_objectives(predictions, batch, stage=stage)
|
105 |
+
return loss.detach()
|
106 |
+
|
107 |
+
def on_stage_start(self, stage, epoch):
|
108 |
+
"""Gets called at the beginning of each epoch"""
|
109 |
+
if stage != sb.Stage.TRAIN:
|
110 |
+
self.cer_metric = self.hparams.cer_computer()
|
111 |
+
self.wer_metric = self.hparams.error_rate_computer()
|
112 |
+
|
113 |
+
def on_stage_end(self, stage, stage_loss, epoch):
|
114 |
+
"""Gets called at the end of an epoch."""
|
115 |
+
# Compute/store important stats
|
116 |
+
stage_stats = {"loss": stage_loss}
|
117 |
+
if stage == sb.Stage.TRAIN:
|
118 |
+
self.train_stats = stage_stats
|
119 |
+
else:
|
120 |
+
stage_stats["CER"] = self.cer_metric.summarize("error_rate")
|
121 |
+
stage_stats["WER"] = self.wer_metric.summarize("error_rate")
|
122 |
+
|
123 |
+
# Perform end-of-iteration things, like annealing, logging, etc.
|
124 |
+
if stage == sb.Stage.VALID:
|
125 |
+
old_lr_model, new_lr_model = self.hparams.lr_annealing_model(
|
126 |
+
stage_stats["loss"]
|
127 |
+
)
|
128 |
+
old_lr_wav2vec, new_lr_wav2vec = self.hparams.lr_annealing_wav2vec(
|
129 |
+
stage_stats["loss"]
|
130 |
+
)
|
131 |
+
sb.nnet.schedulers.update_learning_rate(
|
132 |
+
self.model_optimizer, new_lr_model
|
133 |
+
)
|
134 |
+
sb.nnet.schedulers.update_learning_rate(
|
135 |
+
self.wav2vec_optimizer, new_lr_wav2vec
|
136 |
+
)
|
137 |
+
self.hparams.train_logger.log_stats(
|
138 |
+
stats_meta={
|
139 |
+
"epoch": epoch,
|
140 |
+
"lr_model": old_lr_model,
|
141 |
+
"lr_wav2vec": old_lr_wav2vec,
|
142 |
+
},
|
143 |
+
train_stats=self.train_stats,
|
144 |
+
valid_stats=stage_stats,
|
145 |
+
)
|
146 |
+
self.checkpointer.save_and_keep_only(
|
147 |
+
meta={"WER": stage_stats["WER"]}, min_keys=["WER"],
|
148 |
+
)
|
149 |
+
elif stage == sb.Stage.TEST:
|
150 |
+
self.hparams.train_logger.log_stats(
|
151 |
+
stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
|
152 |
+
test_stats=stage_stats,
|
153 |
+
)
|
154 |
+
with open(self.hparams.wer_file, "w") as w:
|
155 |
+
self.wer_metric.write_stats(w)
|
156 |
+
|
157 |
+
def init_optimizers(self):
|
158 |
+
"Initializes the wav2vec2 optimizer and model optimizer"
|
159 |
+
self.wav2vec_optimizer = self.hparams.wav2vec_opt_class(
|
160 |
+
self.modules.wav2vec2.parameters()
|
161 |
+
)
|
162 |
+
self.model_optimizer = self.hparams.model_opt_class(
|
163 |
+
self.hparams.model.parameters()
|
164 |
+
)
|
165 |
+
|
166 |
+
if self.checkpointer is not None:
|
167 |
+
self.checkpointer.add_recoverable(
|
168 |
+
"wav2vec_opt", self.wav2vec_optimizer
|
169 |
+
)
|
170 |
+
self.checkpointer.add_recoverable("modelopt", self.model_optimizer)
|
171 |
+
|
172 |
+
|
173 |
+
def dataio_prepare(hparams):
|
174 |
+
"""This function prepares the datasets to be used in the brain class.
|
175 |
+
It also defines the data processing pipeline through user-defined functions."""
|
176 |
+
data_folder = hparams["data_folder"]
|
177 |
+
|
178 |
+
train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
179 |
+
csv_path=hparams["train_csv"], replacements={"data_root": data_folder},
|
180 |
+
)
|
181 |
+
|
182 |
+
if hparams["sorting"] == "ascending":
|
183 |
+
# we sort training data to speed up training and get better results.
|
184 |
+
train_data = train_data.filtered_sorted(sort_key="duration")
|
185 |
+
# when sorting do not shuffle in dataloader ! otherwise is pointless
|
186 |
+
hparams["train_dataloader_opts"]["shuffle"] = False
|
187 |
+
|
188 |
+
elif hparams["sorting"] == "descending":
|
189 |
+
train_data = train_data.filtered_sorted(
|
190 |
+
sort_key="duration", reverse=True
|
191 |
+
)
|
192 |
+
# when sorting do not shuffle in dataloader ! otherwise is pointless
|
193 |
+
hparams["train_dataloader_opts"]["shuffle"] = False
|
194 |
+
|
195 |
+
elif hparams["sorting"] == "random":
|
196 |
+
pass
|
197 |
+
|
198 |
+
else:
|
199 |
+
raise NotImplementedError(
|
200 |
+
"sorting must be random, ascending or descending"
|
201 |
+
)
|
202 |
+
|
203 |
+
valid_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
204 |
+
csv_path=hparams["valid_csv"], replacements={"data_root": data_folder},
|
205 |
+
)
|
206 |
+
valid_data = valid_data.filtered_sorted(sort_key="duration")
|
207 |
+
|
208 |
+
# test is separate
|
209 |
+
test_datasets = {}
|
210 |
+
for csv_file in hparams["test_csv"]:
|
211 |
+
name = Path(csv_file).stem
|
212 |
+
test_datasets[name] = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
213 |
+
csv_path=csv_file, replacements={"data_root": data_folder}
|
214 |
+
)
|
215 |
+
test_datasets[name] = test_datasets[name].filtered_sorted(
|
216 |
+
sort_key="duration"
|
217 |
+
)
|
218 |
+
|
219 |
+
datasets = [train_data, valid_data] + [i for k, i in test_datasets.items()]
|
220 |
+
|
221 |
+
# 2. Define audio pipeline:
|
222 |
+
@sb.utils.data_pipeline.takes("wav", "sr")
|
223 |
+
@sb.utils.data_pipeline.provides("sig")
|
224 |
+
def audio_pipeline(wav, sr):
|
225 |
+
sig = sb.dataio.dataio.read_audio(wav)
|
226 |
+
sig = resamplers[sr](sig)
|
227 |
+
return sig
|
228 |
+
|
229 |
+
sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline)
|
230 |
+
label_encoder = sb.dataio.encoder.CTCTextEncoder()
|
231 |
+
|
232 |
+
# 3. Define text pipeline:
|
233 |
+
@sb.utils.data_pipeline.takes("wrd")
|
234 |
+
@sb.utils.data_pipeline.provides(
|
235 |
+
"wrd", "char_list", "tokens_list", "tokens_bos", "tokens_eos", "tokens"
|
236 |
+
)
|
237 |
+
def text_pipeline(wrd):
|
238 |
+
yield wrd
|
239 |
+
char_list = list(wrd)
|
240 |
+
yield char_list
|
241 |
+
tokens_list = label_encoder.encode_sequence(char_list)
|
242 |
+
yield tokens_list
|
243 |
+
tokens_bos = torch.LongTensor([hparams["bos_index"]] + (tokens_list))
|
244 |
+
yield tokens_bos
|
245 |
+
tokens_eos = torch.LongTensor(tokens_list + [hparams["eos_index"]])
|
246 |
+
yield tokens_eos
|
247 |
+
tokens = torch.LongTensor(tokens_list)
|
248 |
+
yield tokens
|
249 |
+
|
250 |
+
sb.dataio.dataset.add_dynamic_item(datasets, text_pipeline)
|
251 |
+
|
252 |
+
lab_enc_file = os.path.join(hparams["save_folder"], "label_encoder.txt")
|
253 |
+
special_labels = {
|
254 |
+
"bos_label": hparams["bos_index"],
|
255 |
+
"eos_label": hparams["eos_index"],
|
256 |
+
"blank_label": hparams["blank_index"],
|
257 |
+
}
|
258 |
+
label_encoder.load_or_create(
|
259 |
+
path=lab_enc_file,
|
260 |
+
from_didatasets=[train_data],
|
261 |
+
output_key="char_list",
|
262 |
+
special_labels=special_labels,
|
263 |
+
sequence_input=True,
|
264 |
+
)
|
265 |
+
|
266 |
+
# 4. Set output:
|
267 |
+
sb.dataio.dataset.set_output_keys(
|
268 |
+
datasets,
|
269 |
+
["id", "sig", "wrd", "char_list", "tokens_bos", "tokens_eos", "tokens"],
|
270 |
+
)
|
271 |
+
return train_data, valid_data, test_datasets, label_encoder
|
272 |
+
|
273 |
+
|
274 |
+
if __name__ == "__main__":
|
275 |
+
|
276 |
+
# CLI:
|
277 |
+
hparams_file, run_opts, overrides = sb.parse_arguments(sys.argv[1:])
|
278 |
+
|
279 |
+
# If distributed_launch=True then
|
280 |
+
# create ddp_group with the right communication protocol
|
281 |
+
sb.utils.distributed.ddp_init_group(run_opts)
|
282 |
+
|
283 |
+
with open(hparams_file) as fin:
|
284 |
+
hparams = load_hyperpyyaml(fin, overrides)
|
285 |
+
|
286 |
+
# Create experiment directory
|
287 |
+
sb.create_experiment_directory(
|
288 |
+
experiment_directory=hparams["output_folder"],
|
289 |
+
hyperparams_to_save=hparams_file,
|
290 |
+
overrides=overrides,
|
291 |
+
)
|
292 |
+
|
293 |
+
# Dataset prep (parsing Librispeech)
|
294 |
+
|
295 |
+
resampler_8000 = T.Resample(8000, 16000, dtype=torch.float)
|
296 |
+
|
297 |
+
resampler_44100 =T.Resample(44100, 16000, dtype=torch.float)
|
298 |
+
resampler_32000 =T.Resample(32000, 16000, dtype=torch.float)
|
299 |
+
resampler_48000 =T.Resample(48000, 16000, dtype=torch.float)
|
300 |
+
|
301 |
+
|
302 |
+
resamplers = {"48000": resampler_48000,"8000": resampler_8000, "44100":resampler_44100, "32000":resampler_32000}
|
303 |
+
|
304 |
+
# here we create the datasets objects as well as tokenization and encoding
|
305 |
+
train_data, valid_data, test_datasets, label_encoder = dataio_prepare(
|
306 |
+
hparams
|
307 |
+
)
|
308 |
+
|
309 |
+
# Trainer initialization
|
310 |
+
asr_brain = ASR(
|
311 |
+
modules=hparams["modules"],
|
312 |
+
hparams=hparams,
|
313 |
+
run_opts=run_opts,
|
314 |
+
checkpointer=hparams["checkpointer"],
|
315 |
+
)
|
316 |
+
asr_brain.device= "cpu"
|
317 |
+
asr_brain.modules.to("cpu")
|
318 |
+
|
319 |
+
# We dynamicaly add the tokenizer to our brain class.
|
320 |
+
# NB: This tokenizer corresponds to the one used for the LM!!
|
321 |
+
asr_brain.tokenizer = label_encoder
|
322 |
+
|
323 |
+
# Training
|
324 |
+
asr_brain.fit(
|
325 |
+
asr_brain.hparams.epoch_counter,
|
326 |
+
train_data,
|
327 |
+
valid_data,
|
328 |
+
train_loader_kwargs=hparams["train_dataloader_opts"],
|
329 |
+
valid_loader_kwargs=hparams["valid_dataloader_opts"],
|
330 |
+
)
|
331 |
+
|
332 |
+
# Testing
|
333 |
+
for k in test_datasets.keys(): # keys are test_clean, test_other etc
|
334 |
+
asr_brain.hparams.wer_file = os.path.join(
|
335 |
+
hparams["output_folder"], "wer_{}.txt".format(k)
|
336 |
+
)
|
337 |
+
asr_brain.evaluate(
|
338 |
+
test_datasets[k], test_loader_kwargs=hparams["test_dataloader_opts"]
|
339 |
+
)
|
partly_frozen_splitted_wavlm/1986/env.log
ADDED
@@ -0,0 +1,402 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
SpeechBrain system description
|
2 |
+
==============================
|
3 |
+
Python version:
|
4 |
+
3.8.5 (default, Sep 4 2020, 07:30:14)
|
5 |
+
[GCC 7.3.0]
|
6 |
+
==============================
|
7 |
+
Installed Python packages:
|
8 |
+
abkhazia==1.0
|
9 |
+
absl-py==0.11.0
|
10 |
+
aiohttp==3.8.0
|
11 |
+
aiosignal==1.2.0
|
12 |
+
alabaster==0.7.12
|
13 |
+
alembic==1.7.4
|
14 |
+
altair==4.2.0
|
15 |
+
altgraph==0.17
|
16 |
+
antlr4-python3-runtime==4.8
|
17 |
+
anyio==3.6.2
|
18 |
+
appdirs==1.4.4
|
19 |
+
argcomplete==1.12.2
|
20 |
+
argon2-cffi==20.1.0
|
21 |
+
asgiref==3.6.0
|
22 |
+
astunparse==1.6.3
|
23 |
+
async-generator==1.10
|
24 |
+
async-timeout==4.0.0
|
25 |
+
attrdict==2.0.1
|
26 |
+
attrs==20.3.0
|
27 |
+
audeer==1.16.0
|
28 |
+
audformat==0.11.5
|
29 |
+
audinterface==0.7.0
|
30 |
+
audiofile==1.0.0
|
31 |
+
audiomentations==0.25.0
|
32 |
+
audioread==2.1.9
|
33 |
+
audobject==0.4.14
|
34 |
+
audresample==0.1.6
|
35 |
+
-e git+https://github.com/facebookresearch/WavAugment.git@54afcdb00ccc852c2f030f239f8532c9562b550e#egg=augment
|
36 |
+
autopage==0.4.0
|
37 |
+
Babel==2.9.0
|
38 |
+
backcall==0.2.0
|
39 |
+
beautifulsoup4==4.10.0
|
40 |
+
black==19.10b0
|
41 |
+
bleach==3.3.0
|
42 |
+
boto3==1.20.2
|
43 |
+
botocore==1.23.2
|
44 |
+
braceexpand==0.1.7
|
45 |
+
cachetools==4.2.0
|
46 |
+
certifi @ file:///croot/certifi_1671487769961/work/certifi
|
47 |
+
cffi==1.14.3
|
48 |
+
cfgv==3.2.0
|
49 |
+
chardet==3.0.4
|
50 |
+
charset-normalizer==2.0.7
|
51 |
+
click==7.1.2
|
52 |
+
cliff==3.9.0
|
53 |
+
clldutils==3.5.4
|
54 |
+
cmaes==0.8.2
|
55 |
+
cmake==3.18.4.post1
|
56 |
+
cmd2==2.2.0
|
57 |
+
colorama==0.4.4
|
58 |
+
colorlog==4.6.2
|
59 |
+
configparser==5.1.0
|
60 |
+
cryptography==38.0.4
|
61 |
+
csvw==1.8.1
|
62 |
+
cycler==0.10.0
|
63 |
+
Cython==0.29.21
|
64 |
+
dataclasses==0.6
|
65 |
+
datasets==1.5.0
|
66 |
+
decorator==4.4.2
|
67 |
+
deepspeech==0.9.1
|
68 |
+
defusedxml==0.7.1
|
69 |
+
denoiser==0.1.5
|
70 |
+
dill==0.3.3
|
71 |
+
Distance==0.1.3
|
72 |
+
distlib==0.3.1
|
73 |
+
Django==3.2.16
|
74 |
+
django-auditlog==2.2.1
|
75 |
+
django-filter==22.1
|
76 |
+
django-js-asset==1.2.2
|
77 |
+
django-mptt==0.14.0
|
78 |
+
djangorestframework==3.14.0
|
79 |
+
docker-pycreds==0.4.0
|
80 |
+
docopt==0.6.2
|
81 |
+
docutils==0.16
|
82 |
+
drf-excel==2.2.0
|
83 |
+
drf-flex-fields==1.0.0
|
84 |
+
drf-renderer-xlsx==0.4.1
|
85 |
+
easyocr==1.2.1
|
86 |
+
editdistance==0.6.0
|
87 |
+
emoji==2.2.0
|
88 |
+
entrypoints==0.3
|
89 |
+
et-xmlfile==1.1.0
|
90 |
+
exceptiongroup==1.1.0
|
91 |
+
farasapy==0.0.14
|
92 |
+
fastapi==0.89.0
|
93 |
+
fasttext==0.9.2
|
94 |
+
ffmpeg-python==0.2.0
|
95 |
+
ffmpy==0.3.0
|
96 |
+
filelock==3.0.12
|
97 |
+
flake8==3.7.9
|
98 |
+
flatbuffers==1.12
|
99 |
+
frozendict==2.0.7
|
100 |
+
frozenlist==1.2.0
|
101 |
+
fsspec==2021.11.0
|
102 |
+
future==0.18.2
|
103 |
+
g2p-en==2.1.0
|
104 |
+
gast==0.3.3
|
105 |
+
gdown==4.2.0
|
106 |
+
gensim==4.0.1
|
107 |
+
gitdb==4.0.9
|
108 |
+
GitPython==3.1.24
|
109 |
+
google-auth==1.24.0
|
110 |
+
google-auth-oauthlib==0.4.2
|
111 |
+
google-pasta==0.2.0
|
112 |
+
gradio==3.16.0
|
113 |
+
greenlet==1.1.2
|
114 |
+
grpcio==1.32.0
|
115 |
+
h11==0.14.0
|
116 |
+
h5features==1.3.2
|
117 |
+
h5py==2.10.0
|
118 |
+
htk-io==0.5
|
119 |
+
httpcore==0.16.3
|
120 |
+
httpx==0.23.3
|
121 |
+
huggingface-hub==0.9.1
|
122 |
+
hydra-colorlog==0.1.4
|
123 |
+
hydra-core==0.11.3
|
124 |
+
HyperPyYAML==1.1.0
|
125 |
+
hypothesis==6.61.2
|
126 |
+
identify==1.5.10
|
127 |
+
idna==2.10
|
128 |
+
imageio==2.9.0
|
129 |
+
imagesize==1.2.0
|
130 |
+
importlib-metadata==4.8.1
|
131 |
+
importlib-resources==5.2.2
|
132 |
+
inflect==5.3.0
|
133 |
+
ipadic==1.0.0
|
134 |
+
ipykernel==5.3.4
|
135 |
+
ipython==7.19.0
|
136 |
+
ipython-genutils==0.2.0
|
137 |
+
ipywebrtc==0.6.0
|
138 |
+
ipywidgets==7.6.3
|
139 |
+
iso-639==0.4.5
|
140 |
+
isodate==0.6.0
|
141 |
+
isort==4.3.21
|
142 |
+
jedi==0.17.2
|
143 |
+
jieba==0.42.1
|
144 |
+
Jinja2==2.11.2
|
145 |
+
jiwer==2.2.0
|
146 |
+
jmespath==0.10.0
|
147 |
+
joblib==0.17.0
|
148 |
+
jsonschema==3.2.0
|
149 |
+
julius==0.2.7
|
150 |
+
jupyter-client==6.1.7
|
151 |
+
jupyter-core==4.7.0
|
152 |
+
jupyterlab-pygments==0.1.2
|
153 |
+
jupyterlab-widgets==1.0.0
|
154 |
+
kaitaistruct==0.9
|
155 |
+
kaldi-io==0.9.4
|
156 |
+
kaldi-python-io==1.2.2
|
157 |
+
kaldiio==2.17.2
|
158 |
+
kenlm @ https://github.com/kpu/kenlm/archive/master.zip
|
159 |
+
Keras-Preprocessing==1.1.2
|
160 |
+
kiwisolver==1.3.1
|
161 |
+
lang-trans==0.6.0
|
162 |
+
latexcodec==2.0.1
|
163 |
+
ldap3==2.9.1
|
164 |
+
librosa==0.9.0
|
165 |
+
linkify-it-py==1.0.3
|
166 |
+
llvmlite==0.35.0
|
167 |
+
lxml==4.9.0
|
168 |
+
Mako==1.1.5
|
169 |
+
Markdown==3.3.3
|
170 |
+
markdown-it-py==2.1.0
|
171 |
+
MarkupSafe==1.1.1
|
172 |
+
marshmallow==3.14.0
|
173 |
+
matplotlib==3.3.3
|
174 |
+
mccabe==0.6.1
|
175 |
+
mcd==0.4
|
176 |
+
mdit-py-plugins==0.3.3
|
177 |
+
mdurl==0.1.2
|
178 |
+
mecab-python3==1.0.3
|
179 |
+
megatron-lm==2.2.0
|
180 |
+
mido==1.2.10
|
181 |
+
mistune==0.8.4
|
182 |
+
more-itertools==8.6.0
|
183 |
+
mpmath==1.2.1
|
184 |
+
multidict==5.2.0
|
185 |
+
multiprocess==0.70.11.1
|
186 |
+
nbclient==0.5.3
|
187 |
+
nbconvert==6.0.7
|
188 |
+
nbformat==5.1.3
|
189 |
+
NEMO==4.3.2
|
190 |
+
nemo-toolkit==1.4.0
|
191 |
+
nest-asyncio==1.5.1
|
192 |
+
networkx==2.5
|
193 |
+
nltk==3.5
|
194 |
+
nodeenv==1.5.0
|
195 |
+
notebook==6.3.0
|
196 |
+
numba==0.52.0
|
197 |
+
numpy==1.19.4
|
198 |
+
nvidia-cublas-cu11==11.10.3.66
|
199 |
+
nvidia-cuda-nvrtc-cu11==11.7.99
|
200 |
+
nvidia-cuda-runtime-cu11==11.7.99
|
201 |
+
nvidia-cudnn-cu11==8.5.0.96
|
202 |
+
oauthlib==3.1.0
|
203 |
+
omegaconf==1.4.1
|
204 |
+
onnx==1.10.2
|
205 |
+
OpenCC==1.1.2
|
206 |
+
opencv-python==4.4.0.46
|
207 |
+
openpyxl==3.0.9
|
208 |
+
opensmile==2.2.0
|
209 |
+
opt-einsum==3.3.0
|
210 |
+
optuna==2.10.0
|
211 |
+
orjson==3.8.4
|
212 |
+
oyaml==1.0
|
213 |
+
packaging==22.0
|
214 |
+
pandas==1.2.5
|
215 |
+
pandocfilters==1.4.3
|
216 |
+
pangu==4.0.6.1
|
217 |
+
parameterized==0.8.1
|
218 |
+
parso==0.7.1
|
219 |
+
pathspec==0.8.1
|
220 |
+
pathtools==0.1.2
|
221 |
+
pbr==5.6.0
|
222 |
+
pefile==2019.4.18
|
223 |
+
pescador==2.1.0
|
224 |
+
pesq==0.0.3
|
225 |
+
pexpect==4.8.0
|
226 |
+
phonemizer==2.2.1
|
227 |
+
pickleshare==0.7.5
|
228 |
+
Pillow==9.3.0
|
229 |
+
pip-api==0.0.23
|
230 |
+
pipreqs==0.4.11
|
231 |
+
pluggy==0.13.1
|
232 |
+
pooch==1.3.0
|
233 |
+
portalocker==2.3.2
|
234 |
+
pre-commit==2.9.0
|
235 |
+
pretty-midi==0.2.9
|
236 |
+
prettytable==2.2.1
|
237 |
+
progressbar2==3.53.1
|
238 |
+
prometheus-client==0.10.1
|
239 |
+
promise==2.3
|
240 |
+
prompt-toolkit==3.0.8
|
241 |
+
protobuf==3.14.0
|
242 |
+
psutil==5.6.6
|
243 |
+
ptyprocess==0.6.0
|
244 |
+
py==1.9.0
|
245 |
+
py-espeak-ng==0.1.8
|
246 |
+
pyannote.audio==1.1.1
|
247 |
+
pyannote.core==4.3
|
248 |
+
pyannote.database==4.1.1
|
249 |
+
pyannote.metrics==3.1
|
250 |
+
pyannote.pipeline==1.5.2
|
251 |
+
PyArabic==0.6.15
|
252 |
+
pyarrow==3.0.0
|
253 |
+
pyasn1==0.4.8
|
254 |
+
pyasn1-modules==0.2.8
|
255 |
+
pybind11==2.8.1
|
256 |
+
pybtex==0.24.0
|
257 |
+
pybtex-docutils==1.0.1
|
258 |
+
pycodestyle==2.5.0
|
259 |
+
pycparser==2.20
|
260 |
+
pycryptodome==3.16.0
|
261 |
+
pyctcdecode==0.4.0
|
262 |
+
pydantic==1.10.4
|
263 |
+
pyDeprecate==0.3.1
|
264 |
+
pydub==0.25.1
|
265 |
+
pyflakes==2.1.1
|
266 |
+
Pygments==2.7.2
|
267 |
+
pygtrie==2.5.0
|
268 |
+
pymodbus==2.5.3
|
269 |
+
pyparsing==2.4.7
|
270 |
+
pyperclip==1.8.2
|
271 |
+
pypinyin==0.43.0
|
272 |
+
pyrsistent==0.17.3
|
273 |
+
pyserial==3.5
|
274 |
+
PySocks==1.7.1
|
275 |
+
pystoi==0.3.3
|
276 |
+
pytest==5.4.1
|
277 |
+
pytest-runner==5.3.1
|
278 |
+
python-bidi==0.4.2
|
279 |
+
python-crfsuite==0.9.7
|
280 |
+
python-dateutil==2.8.2
|
281 |
+
python-Levenshtein==0.12.2
|
282 |
+
python-multipart==0.0.5
|
283 |
+
python-utils==2.4.0
|
284 |
+
pytorch-lightning==1.4.9
|
285 |
+
pytube==11.0.1
|
286 |
+
pytz==2022.6
|
287 |
+
PyWavelets==1.1.1
|
288 |
+
PyYAML==5.3.1
|
289 |
+
pyzmq==20.0.0
|
290 |
+
rapidfuzz==1.8.2
|
291 |
+
regex==2020.11.13
|
292 |
+
requests==2.28.1
|
293 |
+
requests-oauthlib==1.3.0
|
294 |
+
resampy==0.2.2
|
295 |
+
rfc3986==1.4.0
|
296 |
+
rsa==4.7
|
297 |
+
ruamel.yaml==0.17.21
|
298 |
+
ruamel.yaml.clib==0.2.7
|
299 |
+
s3m==1.1.0
|
300 |
+
s3transfer==0.5.0
|
301 |
+
sacrebleu==2.0.0
|
302 |
+
sacremoses==0.0.44
|
303 |
+
scikit-image==0.18.1
|
304 |
+
scikit-learn==0.23.2
|
305 |
+
scipy==1.5.4
|
306 |
+
-e git+https://github.com/sanghack81/SDCIT@00d060dde733fde9345154a494f81e97fb395ca7#egg=SDCIT
|
307 |
+
seaborn==0.11.1
|
308 |
+
segments==2.1.3
|
309 |
+
Send2Trash==1.5.0
|
310 |
+
sentencepiece==0.1.94
|
311 |
+
sentry-sdk==1.4.3
|
312 |
+
shellingham==1.4.0
|
313 |
+
shortuuid==1.0.7
|
314 |
+
SIDEKIT==1.3.8.5.2
|
315 |
+
simplejson==3.17.5
|
316 |
+
six==1.15.0
|
317 |
+
smart-open==5.0.0
|
318 |
+
smmap==5.0.0
|
319 |
+
sniffio==1.3.0
|
320 |
+
snowballstemmer==2.0.0
|
321 |
+
sortedcollections==2.1.0
|
322 |
+
sortedcontainers==2.4.0
|
323 |
+
sounddevice==0.4.5
|
324 |
+
SoundFile==0.10.3.post1
|
325 |
+
soupsieve==2.3
|
326 |
+
sox==1.4.1
|
327 |
+
sparsemax==0.1.9
|
328 |
+
speechbrain==0.5.13
|
329 |
+
sphfile==1.0.3
|
330 |
+
Sphinx==3.3.1
|
331 |
+
sphinx-rtd-theme==0.4.3
|
332 |
+
sphinxcontrib-applehelp==1.0.2
|
333 |
+
sphinxcontrib-bibtex==2.4.1
|
334 |
+
sphinxcontrib-devhelp==1.0.2
|
335 |
+
sphinxcontrib-htmlhelp==1.0.3
|
336 |
+
sphinxcontrib-jsmath==1.0.1
|
337 |
+
sphinxcontrib-qthelp==1.0.3
|
338 |
+
sphinxcontrib-serializinghtml==1.1.4
|
339 |
+
SQLAlchemy==1.4.25
|
340 |
+
sqlparse==0.4.2
|
341 |
+
stanza==1.4.2
|
342 |
+
starlette==0.22.0
|
343 |
+
stevedore==3.4.0
|
344 |
+
subprocess32==3.5.4
|
345 |
+
sympy==1.9
|
346 |
+
tabulate==0.8.9
|
347 |
+
tensorboard==2.4.0
|
348 |
+
tensorboard-plugin-wit==1.7.0
|
349 |
+
tensorflow==2.4.0
|
350 |
+
tensorflow-estimator==2.4.0
|
351 |
+
termcolor==1.1.0
|
352 |
+
terminado==0.9.4
|
353 |
+
testpath==0.4.4
|
354 |
+
threadpoolctl==2.1.0
|
355 |
+
tifffile==2020.12.8
|
356 |
+
tikzplotlib==0.9.8
|
357 |
+
tkseem==0.0.3
|
358 |
+
tokenizers==0.10.2
|
359 |
+
toml==0.10.2
|
360 |
+
toolz==0.12.0
|
361 |
+
torch==1.13.1
|
362 |
+
torch-stft==0.1.4
|
363 |
+
torchaudio==0.13.1
|
364 |
+
torchmetrics==0.6.0
|
365 |
+
torchvision==0.14.1
|
366 |
+
tornado==6.1
|
367 |
+
tqdm==4.61.1
|
368 |
+
trackrip==1.2.1
|
369 |
+
traitlets==5.0.5
|
370 |
+
transformers==4.15.0
|
371 |
+
typed-ast==1.4.1
|
372 |
+
typer==0.4.0
|
373 |
+
typing-extensions==4.4.0
|
374 |
+
uc-micro-py==1.0.1
|
375 |
+
Unidecode==1.3.2
|
376 |
+
uritemplate==3.0.1
|
377 |
+
urllib3==1.26.2
|
378 |
+
uvicorn==0.20.0
|
379 |
+
virtualenv==20.2.1
|
380 |
+
wandb==0.12.6
|
381 |
+
wcwidth==0.2.5
|
382 |
+
webdataset==0.1.62
|
383 |
+
webencodings==0.5.1
|
384 |
+
websockets==10.4
|
385 |
+
Werkzeug==1.0.1
|
386 |
+
wget==3.2
|
387 |
+
widgetsnbextension==3.5.1
|
388 |
+
wordninja==2.0.0
|
389 |
+
wrapt==1.12.1
|
390 |
+
xmltodict==0.13.0
|
391 |
+
xxhash==2.0.0
|
392 |
+
yamllint==1.23.0
|
393 |
+
yarg==0.1.9
|
394 |
+
yarl==1.7.2
|
395 |
+
yaspin==2.1.0
|
396 |
+
youtokentome==1.0.6
|
397 |
+
youtube-dl==2021.6.6
|
398 |
+
zipp==3.6.0
|
399 |
+
==============================
|
400 |
+
Could not get git revision==============================
|
401 |
+
CUDA version:
|
402 |
+
11.7
|
partly_frozen_splitted_wavlm/1986/hyperparams.yaml
ADDED
@@ -0,0 +1,162 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Generated 2023-01-08 from:
|
2 |
+
# /home/salah/kenlm_train/to_copy/wavlm_partly_frozen.yaml
|
3 |
+
# yamllint disable
|
4 |
+
# ################################
|
5 |
+
# Model: wav2vec2 + DNN + CTC
|
6 |
+
# Augmentation: SpecAugment
|
7 |
+
# Authors: Sung-Lin Yeh 2021
|
8 |
+
# ################################
|
9 |
+
|
10 |
+
# Seed needs to be set at top of yaml, before objects with parameters are made
|
11 |
+
seed: 1986
|
12 |
+
__set_seed: !apply:torch.manual_seed [1986]
|
13 |
+
output_folder: partly_frozen_splitted_wavlm/1986/
|
14 |
+
wer_file: partly_frozen_splitted_wavlm/1986//wer.txt
|
15 |
+
save_folder: partly_frozen_splitted_wavlm/1986//save
|
16 |
+
train_log: partly_frozen_splitted_wavlm/1986//train_log.txt
|
17 |
+
|
18 |
+
# URL for the biggest Fairseq english wav2vec2 model.
|
19 |
+
|
20 |
+
# Data files
|
21 |
+
data_folder: /gpfsscratch/rech/nou/uzn19yk/Libri/LibriSpeech/ # e,g./path/to/LibriSpeech
|
22 |
+
# noise/ris dataset will automatically be downloaded
|
23 |
+
data_folder_rirs: /gpfsscratch/rech/nou/uzn19yk/Libri/LibriSpeech/
|
24 |
+
train_splits: [train-clean-100]
|
25 |
+
dev_splits: [dev-clean]
|
26 |
+
test_splits: [test-clean, test-other]
|
27 |
+
skip_prep: false
|
28 |
+
ckpt_interval_minutes: 25 # save checkpoint every N min
|
29 |
+
csv_folder: /gpfsstore/rech/nou/uzn19yk/iwslt/splitted_clean_tunisian_csvs/
|
30 |
+
train_csv: test_salah_local.csv
|
31 |
+
valid_csv: test_salah_local.csv
|
32 |
+
test_csv:
|
33 |
+
- test_salah_local.csv
|
34 |
+
|
35 |
+
# Training parameters
|
36 |
+
number_of_epochs: 12
|
37 |
+
lr: 1
|
38 |
+
lr_wav2vec: 0.0001
|
39 |
+
sorting: ascending
|
40 |
+
auto_mix_prec: false
|
41 |
+
sample_rate: 16000
|
42 |
+
|
43 |
+
avoid_if_longer_than: 10
|
44 |
+
# With data_parallel batch_size is split into N jobs
|
45 |
+
# With DDP batch_size is multiplied by N jobs
|
46 |
+
# Must be 3 per GPU to fit 32GB of VRAM
|
47 |
+
batch_size: 1
|
48 |
+
test_batch_size: 1
|
49 |
+
|
50 |
+
# Dataloader options
|
51 |
+
train_dataloader_opts:
|
52 |
+
batch_size: 1
|
53 |
+
|
54 |
+
valid_dataloader_opts:
|
55 |
+
batch_size: 1
|
56 |
+
|
57 |
+
test_dataloader_opts:
|
58 |
+
batch_size: 1
|
59 |
+
|
60 |
+
# Model parameters
|
61 |
+
activation: &id001 !name:torch.nn.LeakyReLU
|
62 |
+
dnn_layers: 2
|
63 |
+
dnn_neurons: 1024
|
64 |
+
freeze_wav2vec: false
|
65 |
+
|
66 |
+
# Outputs
|
67 |
+
output_neurons: 41 # BPE size, index(blank/eos/bos) = 0
|
68 |
+
|
69 |
+
# Decoding parameters
|
70 |
+
blank_index: 0
|
71 |
+
bos_index: 1
|
72 |
+
eos_index: 2
|
73 |
+
|
74 |
+
#
|
75 |
+
# Functions and classes
|
76 |
+
#
|
77 |
+
epoch_counter: &id008 !new:speechbrain.utils.epoch_loop.EpochCounter
|
78 |
+
|
79 |
+
limit: 12
|
80 |
+
|
81 |
+
augmentation: !new:speechbrain.lobes.augment.TimeDomainSpecAugment
|
82 |
+
sample_rate: 16000
|
83 |
+
speeds: [95, 100, 105]
|
84 |
+
|
85 |
+
enc: &id003 !new:speechbrain.lobes.models.VanillaNN.VanillaNN
|
86 |
+
input_shape: [null, null, 1024]
|
87 |
+
activation: *id001
|
88 |
+
dnn_blocks: 2
|
89 |
+
dnn_neurons: 1024
|
90 |
+
|
91 |
+
wav2vec2: &id002 !new:speechbrain.lobes.models.huggingface_wav2vec.HuggingFaceWav2Vec2
|
92 |
+
source: wavlm-large/
|
93 |
+
output_norm: true
|
94 |
+
freeze: false
|
95 |
+
freeze_feature_extractor: true
|
96 |
+
save_path: partly_frozen_splitted_wavlm/1986//save/wav2vec2_hubert_checkpoint
|
97 |
+
|
98 |
+
#####
|
99 |
+
# Uncomment this block if you prefer to use a Fairseq pretrained model instead
|
100 |
+
# of a HuggingFace one. Here, we provide an URL that is obtained from the
|
101 |
+
# Fairseq github for the multilingual XLSR.
|
102 |
+
#
|
103 |
+
#wav2vec2_url: https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_960h_pl.pt
|
104 |
+
#wav2vec2: !new:speechbrain.lobes.models.fairseq_wav2vec.FairseqWav2Vec2
|
105 |
+
# pretrained_path: !ref <wav2vec2_url>
|
106 |
+
# output_norm: True
|
107 |
+
# freeze: False
|
108 |
+
# save_path: !ref <save_folder>/wav2vec2_checkpoint/model.pt
|
109 |
+
|
110 |
+
ctc_lin: &id004 !new:speechbrain.nnet.linear.Linear
|
111 |
+
|
112 |
+
input_size: 1024
|
113 |
+
n_neurons: 41
|
114 |
+
|
115 |
+
log_softmax: !new:speechbrain.nnet.activations.Softmax
|
116 |
+
apply_log: true
|
117 |
+
|
118 |
+
ctc_cost: !name:speechbrain.nnet.losses.ctc_loss
|
119 |
+
blank_index: 0
|
120 |
+
|
121 |
+
modules:
|
122 |
+
wav2vec2: *id002
|
123 |
+
enc: *id003
|
124 |
+
ctc_lin: *id004
|
125 |
+
model: &id005 !new:torch.nn.ModuleList
|
126 |
+
- [*id003, *id004]
|
127 |
+
model_opt_class: !name:torch.optim.Adadelta
|
128 |
+
lr: 1
|
129 |
+
rho: 0.95
|
130 |
+
eps: 1.e-8
|
131 |
+
|
132 |
+
wav2vec_opt_class: !name:torch.optim.Adam
|
133 |
+
lr: 0.0001
|
134 |
+
|
135 |
+
lr_annealing_model: &id006 !new:speechbrain.nnet.schedulers.NewBobScheduler
|
136 |
+
initial_value: 1
|
137 |
+
improvement_threshold: 0.0025
|
138 |
+
annealing_factor: 0.8
|
139 |
+
patient: 0
|
140 |
+
|
141 |
+
lr_annealing_wav2vec: &id007 !new:speechbrain.nnet.schedulers.NewBobScheduler
|
142 |
+
initial_value: 0.0001
|
143 |
+
improvement_threshold: 0.0025
|
144 |
+
annealing_factor: 0.9
|
145 |
+
patient: 0
|
146 |
+
|
147 |
+
|
148 |
+
checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
|
149 |
+
checkpoints_dir: partly_frozen_splitted_wavlm/1986//save
|
150 |
+
recoverables:
|
151 |
+
wav2vec2: *id002
|
152 |
+
model: *id005
|
153 |
+
scheduler_model: *id006
|
154 |
+
scheduler_wav2vec: *id007
|
155 |
+
counter: *id008
|
156 |
+
train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
|
157 |
+
save_file: partly_frozen_splitted_wavlm/1986//train_log.txt
|
158 |
+
|
159 |
+
error_rate_computer: !name:speechbrain.utils.metric_stats.ErrorRateStats
|
160 |
+
|
161 |
+
cer_computer: !name:speechbrain.utils.metric_stats.ErrorRateStats
|
162 |
+
split_tokens: true
|
partly_frozen_splitted_wavlm/1986/lm_decoded_ctc.py
ADDED
@@ -0,0 +1,377 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env/python3
|
2 |
+
"""Recipe for training a wav2vec-based ctc ASR system with librispeech.
|
3 |
+
The system employs wav2vec as its encoder. Decoding is performed with
|
4 |
+
ctc greedy decoder.
|
5 |
+
To run this recipe, do the following:
|
6 |
+
> python train_with_wav2vec.py hparams/train_with_wav2vec.yaml
|
7 |
+
The neural network is trained on CTC likelihood target and character units
|
8 |
+
are used as basic recognition tokens. Training is performed on the full
|
9 |
+
LibriSpeech dataset (960 h).
|
10 |
+
|
11 |
+
Authors
|
12 |
+
* Sung-Lin Yeh 2021
|
13 |
+
* Titouan Parcollet 2021
|
14 |
+
* Ju-Chieh Chou 2020
|
15 |
+
* Mirco Ravanelli 2020
|
16 |
+
* Abdel Heba 2020
|
17 |
+
* Peter Plantinga 2020
|
18 |
+
* Samuele Cornell 2020
|
19 |
+
"""
|
20 |
+
|
21 |
+
import os
|
22 |
+
import sys
|
23 |
+
import torch
|
24 |
+
import logging
|
25 |
+
import speechbrain as sb
|
26 |
+
from speechbrain.utils.distributed import run_on_main
|
27 |
+
from hyperpyyaml import load_hyperpyyaml
|
28 |
+
from pathlib import Path
|
29 |
+
from pyctcdecode import build_ctcdecoder
|
30 |
+
|
31 |
+
logger = logging.getLogger(__name__)
|
32 |
+
|
33 |
+
|
34 |
+
# Define training procedure
|
35 |
+
class ASR(sb.Brain):
|
36 |
+
def compute_forward(self, batch, stage):
|
37 |
+
"""Forward computations from the waveform batches to the output probabilities."""
|
38 |
+
batch = batch.to(self.device)
|
39 |
+
wavs, wav_lens = batch.sig
|
40 |
+
tokens_bos, _ = batch.tokens_bos
|
41 |
+
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
|
42 |
+
|
43 |
+
# Forward pass
|
44 |
+
feats = self.modules.wav2vec2(wavs)
|
45 |
+
|
46 |
+
x = self.modules.enc(feats.detach())[0]
|
47 |
+
#x = self.modules.enc(feats.detach())
|
48 |
+
# Compute outputs
|
49 |
+
p_tokens = None
|
50 |
+
logits = self.modules.ctc_lin(x)
|
51 |
+
p_ctc = self.hparams.log_softmax(logits)
|
52 |
+
if stage != sb.Stage.TRAIN:
|
53 |
+
p_tokens = sb.decoders.ctc_greedy_decode(
|
54 |
+
p_ctc, wav_lens, blank_id=self.hparams.blank_index
|
55 |
+
)
|
56 |
+
return p_ctc, wav_lens, p_tokens
|
57 |
+
|
58 |
+
def compute_objectives(self, predictions, batch, stage):
|
59 |
+
"""Computes the loss (CTC+NLL) given predictions and targets."""
|
60 |
+
|
61 |
+
p_ctc, wav_lens, predicted_tokens = predictions
|
62 |
+
|
63 |
+
ids = batch.id
|
64 |
+
tokens_eos, tokens_eos_lens = batch.tokens_eos
|
65 |
+
tokens, tokens_lens = batch.tokens
|
66 |
+
|
67 |
+
if hasattr(self.modules, "env_corrupt") and stage == sb.Stage.TRAIN:
|
68 |
+
tokens_eos = torch.cat([tokens_eos, tokens_eos], dim=0)
|
69 |
+
tokens_eos_lens = torch.cat(
|
70 |
+
[tokens_eos_lens, tokens_eos_lens], dim=0
|
71 |
+
)
|
72 |
+
tokens = torch.cat([tokens, tokens], dim=0)
|
73 |
+
tokens_lens = torch.cat([tokens_lens, tokens_lens], dim=0)
|
74 |
+
|
75 |
+
loss_ctc = self.hparams.ctc_cost(p_ctc, tokens, wav_lens, tokens_lens)
|
76 |
+
loss = loss_ctc
|
77 |
+
|
78 |
+
if stage != sb.Stage.TRAIN:
|
79 |
+
# Decode token terms to words
|
80 |
+
predicted_words = [
|
81 |
+
"".join(self.tokenizer.decode_ndim(utt_seq)).split(" ")
|
82 |
+
for utt_seq in predicted_tokens
|
83 |
+
]
|
84 |
+
predicted_words =[]
|
85 |
+
for logs in p_ctc:
|
86 |
+
text = decoder.decode(logs.detach().cpu().numpy())
|
87 |
+
predicted_words.append(text.split(" "))
|
88 |
+
|
89 |
+
target_words = [wrd.split(" ") for wrd in batch.wrd]
|
90 |
+
self.wer_metric.append(ids, predicted_words, target_words)
|
91 |
+
self.cer_metric.append(ids, predicted_words, target_words)
|
92 |
+
|
93 |
+
return loss
|
94 |
+
|
95 |
+
def fit_batch(self, batch):
|
96 |
+
"""Train the parameters given a single batch in input"""
|
97 |
+
predictions = self.compute_forward(batch, sb.Stage.TRAIN)
|
98 |
+
loss = self.compute_objectives(predictions, batch, sb.Stage.TRAIN)
|
99 |
+
loss.backward()
|
100 |
+
if self.check_gradients(loss):
|
101 |
+
self.wav2vec_optimizer.step()
|
102 |
+
self.model_optimizer.step()
|
103 |
+
|
104 |
+
self.wav2vec_optimizer.zero_grad()
|
105 |
+
self.model_optimizer.zero_grad()
|
106 |
+
|
107 |
+
return loss.detach()
|
108 |
+
|
109 |
+
def evaluate_batch(self, batch, stage):
|
110 |
+
"""Computations needed for validation/test batches"""
|
111 |
+
predictions = self.compute_forward(batch, stage=stage)
|
112 |
+
with torch.no_grad():
|
113 |
+
loss = self.compute_objectives(predictions, batch, stage=stage)
|
114 |
+
return loss.detach()
|
115 |
+
|
116 |
+
def on_stage_start(self, stage, epoch):
|
117 |
+
"""Gets called at the beginning of each epoch"""
|
118 |
+
if stage != sb.Stage.TRAIN:
|
119 |
+
self.cer_metric = self.hparams.cer_computer()
|
120 |
+
self.wer_metric = self.hparams.error_rate_computer()
|
121 |
+
|
122 |
+
def on_stage_end(self, stage, stage_loss, epoch):
|
123 |
+
"""Gets called at the end of an epoch."""
|
124 |
+
# Compute/store important stats
|
125 |
+
stage_stats = {"loss": stage_loss}
|
126 |
+
if stage == sb.Stage.TRAIN:
|
127 |
+
self.train_stats = stage_stats
|
128 |
+
else:
|
129 |
+
stage_stats["CER"] = self.cer_metric.summarize("error_rate")
|
130 |
+
stage_stats["WER"] = self.wer_metric.summarize("error_rate")
|
131 |
+
|
132 |
+
# Perform end-of-iteration things, like annealing, logging, etc.
|
133 |
+
if stage == sb.Stage.VALID:
|
134 |
+
old_lr_model, new_lr_model = self.hparams.lr_annealing_model(
|
135 |
+
stage_stats["loss"]
|
136 |
+
)
|
137 |
+
old_lr_wav2vec, new_lr_wav2vec = self.hparams.lr_annealing_wav2vec(
|
138 |
+
stage_stats["loss"]
|
139 |
+
)
|
140 |
+
sb.nnet.schedulers.update_learning_rate(
|
141 |
+
self.model_optimizer, new_lr_model
|
142 |
+
)
|
143 |
+
sb.nnet.schedulers.update_learning_rate(
|
144 |
+
self.wav2vec_optimizer, new_lr_wav2vec
|
145 |
+
)
|
146 |
+
self.hparams.train_logger.log_stats(
|
147 |
+
stats_meta={
|
148 |
+
"epoch": epoch,
|
149 |
+
"lr_model": old_lr_model,
|
150 |
+
"lr_wav2vec": old_lr_wav2vec,
|
151 |
+
},
|
152 |
+
train_stats=self.train_stats,
|
153 |
+
valid_stats=stage_stats,
|
154 |
+
)
|
155 |
+
self.checkpointer.save_and_keep_only(
|
156 |
+
meta={"WER": stage_stats["WER"]}, min_keys=["WER"],
|
157 |
+
)
|
158 |
+
elif stage == sb.Stage.TEST:
|
159 |
+
self.hparams.train_logger.log_stats(
|
160 |
+
stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
|
161 |
+
test_stats=stage_stats,
|
162 |
+
)
|
163 |
+
with open(self.hparams.wer_file, "w") as w:
|
164 |
+
self.wer_metric.write_stats(w)
|
165 |
+
|
166 |
+
def init_optimizers(self):
|
167 |
+
"Initializes the wav2vec2 optimizer and model optimizer"
|
168 |
+
self.wav2vec_optimizer = self.hparams.wav2vec_opt_class(
|
169 |
+
self.modules.wav2vec2.parameters()
|
170 |
+
)
|
171 |
+
self.model_optimizer = self.hparams.model_opt_class(
|
172 |
+
self.hparams.model.parameters()
|
173 |
+
)
|
174 |
+
|
175 |
+
if self.checkpointer is not None:
|
176 |
+
self.checkpointer.add_recoverable(
|
177 |
+
"wav2vec_opt", self.wav2vec_optimizer
|
178 |
+
)
|
179 |
+
self.checkpointer.add_recoverable("modelopt", self.model_optimizer)
|
180 |
+
|
181 |
+
|
182 |
+
def dataio_prepare(hparams):
|
183 |
+
"""This function prepares the datasets to be used in the brain class.
|
184 |
+
It also defines the data processing pipeline through user-defined functions."""
|
185 |
+
data_folder = hparams["data_folder"]
|
186 |
+
|
187 |
+
train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
188 |
+
csv_path=hparams["train_csv"], replacements={"data_root": data_folder},
|
189 |
+
)
|
190 |
+
|
191 |
+
if hparams["sorting"] == "ascending":
|
192 |
+
# we sort training data to speed up training and get better results.
|
193 |
+
train_data = train_data.filtered_sorted(sort_key="duration")
|
194 |
+
# when sorting do not shuffle in dataloader ! otherwise is pointless
|
195 |
+
hparams["train_dataloader_opts"]["shuffle"] = False
|
196 |
+
|
197 |
+
elif hparams["sorting"] == "descending":
|
198 |
+
train_data = train_data.filtered_sorted(
|
199 |
+
sort_key="duration", reverse=True
|
200 |
+
)
|
201 |
+
# when sorting do not shuffle in dataloader ! otherwise is pointless
|
202 |
+
hparams["train_dataloader_opts"]["shuffle"] = False
|
203 |
+
|
204 |
+
elif hparams["sorting"] == "random":
|
205 |
+
pass
|
206 |
+
|
207 |
+
else:
|
208 |
+
raise NotImplementedError(
|
209 |
+
"sorting must be random, ascending or descending"
|
210 |
+
)
|
211 |
+
|
212 |
+
valid_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
213 |
+
csv_path=hparams["valid_csv"], replacements={"data_root": data_folder},
|
214 |
+
)
|
215 |
+
valid_data = valid_data.filtered_sorted(sort_key="duration")
|
216 |
+
|
217 |
+
# test is separate
|
218 |
+
test_datasets = {}
|
219 |
+
for csv_file in hparams["test_csv"]:
|
220 |
+
name = Path(csv_file).stem
|
221 |
+
test_datasets[name] = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
222 |
+
csv_path=csv_file, replacements={"data_root": data_folder}
|
223 |
+
)
|
224 |
+
test_datasets[name] = test_datasets[name].filtered_sorted(
|
225 |
+
sort_key="duration"
|
226 |
+
)
|
227 |
+
|
228 |
+
datasets = [train_data, valid_data] + [i for k, i in test_datasets.items()]
|
229 |
+
|
230 |
+
# 2. Define audio pipeline:
|
231 |
+
@sb.utils.data_pipeline.takes("wav")
|
232 |
+
@sb.utils.data_pipeline.provides("sig")
|
233 |
+
def audio_pipeline(wav):
|
234 |
+
sig = sb.dataio.dataio.read_audio(wav)
|
235 |
+
return sig
|
236 |
+
|
237 |
+
sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline)
|
238 |
+
label_encoder = sb.dataio.encoder.CTCTextEncoder()
|
239 |
+
|
240 |
+
# 3. Define text pipeline:
|
241 |
+
@sb.utils.data_pipeline.takes("wrd")
|
242 |
+
@sb.utils.data_pipeline.provides(
|
243 |
+
"wrd", "char_list", "tokens_list", "tokens_bos", "tokens_eos", "tokens"
|
244 |
+
)
|
245 |
+
def text_pipeline(wrd):
|
246 |
+
yield wrd
|
247 |
+
char_list = list(wrd)
|
248 |
+
yield char_list
|
249 |
+
tokens_list = label_encoder.encode_sequence(char_list)
|
250 |
+
yield tokens_list
|
251 |
+
tokens_bos = torch.LongTensor([hparams["bos_index"]] + (tokens_list))
|
252 |
+
yield tokens_bos
|
253 |
+
tokens_eos = torch.LongTensor(tokens_list + [hparams["eos_index"]])
|
254 |
+
yield tokens_eos
|
255 |
+
tokens = torch.LongTensor(tokens_list)
|
256 |
+
yield tokens
|
257 |
+
|
258 |
+
sb.dataio.dataset.add_dynamic_item(datasets, text_pipeline)
|
259 |
+
|
260 |
+
lab_enc_file = os.path.join(hparams["save_folder"], "label_encoder.txt")
|
261 |
+
special_labels = {
|
262 |
+
"bos_label": hparams["bos_index"],
|
263 |
+
"eos_label": hparams["eos_index"],
|
264 |
+
"blank_label": hparams["blank_index"],
|
265 |
+
}
|
266 |
+
label_encoder.load_or_create(
|
267 |
+
path=lab_enc_file,
|
268 |
+
from_didatasets=[train_data],
|
269 |
+
output_key="char_list",
|
270 |
+
special_labels=special_labels,
|
271 |
+
sequence_input=True,
|
272 |
+
)
|
273 |
+
|
274 |
+
# 4. Set output:
|
275 |
+
sb.dataio.dataset.set_output_keys(
|
276 |
+
datasets,
|
277 |
+
["id", "sig", "wrd", "char_list", "tokens_bos", "tokens_eos", "tokens"],
|
278 |
+
)
|
279 |
+
return train_data, valid_data, test_datasets, label_encoder
|
280 |
+
|
281 |
+
|
282 |
+
if __name__ == "__main__":
|
283 |
+
|
284 |
+
# CLI:
|
285 |
+
hparams_file, run_opts, overrides = sb.parse_arguments(sys.argv[1:])
|
286 |
+
|
287 |
+
# If distributed_launch=True then
|
288 |
+
# create ddp_group with the right communication protocol
|
289 |
+
sb.utils.distributed.ddp_init_group(run_opts)
|
290 |
+
|
291 |
+
with open(hparams_file) as fin:
|
292 |
+
hparams = load_hyperpyyaml(fin, overrides)
|
293 |
+
|
294 |
+
# Create experiment directory
|
295 |
+
sb.create_experiment_directory(
|
296 |
+
experiment_directory=hparams["output_folder"],
|
297 |
+
hyperparams_to_save=hparams_file,
|
298 |
+
overrides=overrides,
|
299 |
+
)
|
300 |
+
def read_labels_file(labels_file):
|
301 |
+
with open(labels_file, "r") as lf:
|
302 |
+
lines = lf.read().splitlines()
|
303 |
+
division = "==="
|
304 |
+
numbers = {}
|
305 |
+
for line in lines :
|
306 |
+
if division in line :
|
307 |
+
break
|
308 |
+
string, number = line.split("=>")
|
309 |
+
number = int(number)
|
310 |
+
string = string[1:-2]
|
311 |
+
numbers[number] = string
|
312 |
+
return [numbers[x] for x in range(len(numbers))]
|
313 |
+
labels = read_labels_file(os.path.join(hparams["save_folder"], "label_encoder.txt"))
|
314 |
+
print(labels)
|
315 |
+
labels = [""] + labels[1:]
|
316 |
+
print(len(labels))
|
317 |
+
decoder = build_ctcdecoder(
|
318 |
+
labels,
|
319 |
+
kenlm_model_path="/gpfsstore/rech/nou/uzn19yk/4-gram.arpa", # either .arpa or .bin file
|
320 |
+
alpha=0.5, # tuned on a val set
|
321 |
+
beta=1.0, # tuned on a val set
|
322 |
+
)
|
323 |
+
|
324 |
+
# Dataset prep (parsing Librispeech)
|
325 |
+
from librispeech_prepare import prepare_librispeech # noqa
|
326 |
+
|
327 |
+
# multi-gpu (ddp) save data preparation
|
328 |
+
"""
|
329 |
+
run_on_main(
|
330 |
+
prepare_librispeech,
|
331 |
+
kwargs={
|
332 |
+
"data_folder": hparams["data_folder"],
|
333 |
+
"tr_splits": hparams["train_splits"],
|
334 |
+
"dev_splits": hparams["dev_splits"],
|
335 |
+
"te_splits": hparams["test_splits"],
|
336 |
+
"save_folder": hparams["output_folder"],
|
337 |
+
"merge_lst": hparams["train_splits"],
|
338 |
+
"merge_name": "train.csv",
|
339 |
+
"skip_prep": hparams["skip_prep"],
|
340 |
+
},
|
341 |
+
)
|
342 |
+
"""
|
343 |
+
|
344 |
+
# here we create the datasets objects as well as tokenization and encoding
|
345 |
+
train_data, valid_data, test_datasets, label_encoder = dataio_prepare(
|
346 |
+
hparams
|
347 |
+
)
|
348 |
+
|
349 |
+
# Trainer initialization
|
350 |
+
asr_brain = ASR(
|
351 |
+
modules=hparams["modules"],
|
352 |
+
hparams=hparams,
|
353 |
+
run_opts=run_opts,
|
354 |
+
checkpointer=hparams["checkpointer"],
|
355 |
+
)
|
356 |
+
|
357 |
+
# We dynamicaly add the tokenizer to our brain class.
|
358 |
+
# NB: This tokenizer corresponds to the one used for the LM!!
|
359 |
+
asr_brain.tokenizer = label_encoder
|
360 |
+
|
361 |
+
# Training
|
362 |
+
asr_brain.fit(
|
363 |
+
asr_brain.hparams.epoch_counter,
|
364 |
+
train_data,
|
365 |
+
valid_data,
|
366 |
+
train_loader_kwargs=hparams["train_dataloader_opts"],
|
367 |
+
valid_loader_kwargs=hparams["valid_dataloader_opts"],
|
368 |
+
)
|
369 |
+
|
370 |
+
# Testing
|
371 |
+
for k in test_datasets.keys(): # keys are test_clean, test_other etc
|
372 |
+
asr_brain.hparams.wer_file = os.path.join(
|
373 |
+
hparams["output_folder"], "wer_{}.txt".format(k)
|
374 |
+
)
|
375 |
+
asr_brain.evaluate(
|
376 |
+
test_datasets[k], test_loader_kwargs=hparams["test_dataloader_opts"]
|
377 |
+
)
|
partly_frozen_splitted_wavlm/1986/lm_tunisian.py
ADDED
@@ -0,0 +1,361 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env/python3
|
2 |
+
"""Recipe for training a wav2vec-based ctc ASR system with librispeech.
|
3 |
+
The system employs wav2vec as its encoder. Decoding is performed with
|
4 |
+
ctc greedy decoder.
|
5 |
+
To run this recipe, do the following:
|
6 |
+
> python train_with_wav2vec.py hparams/train_with_wav2vec.yaml
|
7 |
+
The neural network is trained on CTC likelihood target and character units
|
8 |
+
are used as basic recognition tokens. Training is performed on the full
|
9 |
+
LibriSpeech dataset (960 h).
|
10 |
+
|
11 |
+
Authors
|
12 |
+
* Sung-Lin Yeh 2021
|
13 |
+
* Titouan Parcollet 2021
|
14 |
+
* Ju-Chieh Chou 2020
|
15 |
+
* Mirco Ravanelli 2020
|
16 |
+
* Abdel Heba 2020
|
17 |
+
* Peter Plantinga 2020
|
18 |
+
* Samuele Cornell 2020
|
19 |
+
"""
|
20 |
+
|
21 |
+
import os
|
22 |
+
import sys
|
23 |
+
import torch
|
24 |
+
import logging
|
25 |
+
import speechbrain as sb
|
26 |
+
from speechbrain.utils.distributed import run_on_main
|
27 |
+
from hyperpyyaml import load_hyperpyyaml
|
28 |
+
from pathlib import Path
|
29 |
+
import torchaudio.transforms as T
|
30 |
+
|
31 |
+
from pyctcdecode import build_ctcdecoder
|
32 |
+
logger = logging.getLogger(__name__)
|
33 |
+
|
34 |
+
# Define training procedure
|
35 |
+
class ASR(sb.Brain):
|
36 |
+
def compute_forward(self, batch, stage):
|
37 |
+
"""Forward computations from the waveform batches to the output probabilities."""
|
38 |
+
batch = batch.to(self.device)
|
39 |
+
wavs, wav_lens = batch.sig
|
40 |
+
tokens_bos, _ = batch.tokens_bos
|
41 |
+
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
|
42 |
+
|
43 |
+
# Forward pass
|
44 |
+
feats = self.modules.wav2vec2(wavs)
|
45 |
+
x = self.modules.enc(feats)
|
46 |
+
# Compute outputs
|
47 |
+
p_tokens = None
|
48 |
+
logits = self.modules.ctc_lin(x)
|
49 |
+
p_ctc = self.hparams.log_softmax(logits)
|
50 |
+
if stage != sb.Stage.TRAIN:
|
51 |
+
p_tokens = sb.decoders.ctc_greedy_decode(
|
52 |
+
p_ctc, wav_lens, blank_id=self.hparams.blank_index
|
53 |
+
)
|
54 |
+
return p_ctc, wav_lens, p_tokens
|
55 |
+
|
56 |
+
def compute_objectives(self, predictions, batch, stage):
|
57 |
+
"""Computes the loss (CTC+NLL) given predictions and targets."""
|
58 |
+
|
59 |
+
p_ctc, wav_lens, predicted_tokens = predictions
|
60 |
+
|
61 |
+
ids = batch.id
|
62 |
+
tokens_eos, tokens_eos_lens = batch.tokens_eos
|
63 |
+
tokens, tokens_lens = batch.tokens
|
64 |
+
|
65 |
+
if hasattr(self.modules, "env_corrupt") and stage == sb.Stage.TRAIN:
|
66 |
+
tokens_eos = torch.cat([tokens_eos, tokens_eos], dim=0)
|
67 |
+
tokens_eos_lens = torch.cat(
|
68 |
+
[tokens_eos_lens, tokens_eos_lens], dim=0
|
69 |
+
)
|
70 |
+
tokens = torch.cat([tokens, tokens], dim=0)
|
71 |
+
tokens_lens = torch.cat([tokens_lens, tokens_lens], dim=0)
|
72 |
+
|
73 |
+
loss_ctc = self.hparams.ctc_cost(p_ctc, tokens, wav_lens, tokens_lens)
|
74 |
+
loss = loss_ctc
|
75 |
+
if stage != sb.Stage.TRAIN:
|
76 |
+
# Decode token terms to words
|
77 |
+
predicted_words =[]
|
78 |
+
for logs in p_ctc:
|
79 |
+
text = decoder.decode(logs.detach().cpu().numpy())
|
80 |
+
predicted_words.append(text.split(" "))
|
81 |
+
|
82 |
+
|
83 |
+
target_words = [wrd.split(" ") for wrd in batch.wrd]
|
84 |
+
self.wer_metric.append(ids, predicted_words, target_words)
|
85 |
+
self.cer_metric.append(ids, predicted_words, target_words)
|
86 |
+
|
87 |
+
return loss
|
88 |
+
|
89 |
+
def fit_batch(self, batch):
|
90 |
+
"""Train the parameters given a single batch in input"""
|
91 |
+
predictions = self.compute_forward(batch, sb.Stage.TRAIN)
|
92 |
+
loss = self.compute_objectives(predictions, batch, sb.Stage.TRAIN)
|
93 |
+
loss.backward()
|
94 |
+
if self.check_gradients(loss):
|
95 |
+
self.wav2vec_optimizer.step()
|
96 |
+
self.model_optimizer.step()
|
97 |
+
|
98 |
+
self.wav2vec_optimizer.zero_grad()
|
99 |
+
self.model_optimizer.zero_grad()
|
100 |
+
|
101 |
+
return loss.detach()
|
102 |
+
|
103 |
+
def evaluate_batch(self, batch, stage):
|
104 |
+
"""Computations needed for validation/test batches"""
|
105 |
+
predictions = self.compute_forward(batch, stage=stage)
|
106 |
+
with torch.no_grad():
|
107 |
+
loss = self.compute_objectives(predictions, batch, stage=stage)
|
108 |
+
return loss.detach()
|
109 |
+
|
110 |
+
def on_stage_start(self, stage, epoch):
|
111 |
+
"""Gets called at the beginning of each epoch"""
|
112 |
+
if stage != sb.Stage.TRAIN:
|
113 |
+
self.cer_metric = self.hparams.cer_computer()
|
114 |
+
self.wer_metric = self.hparams.error_rate_computer()
|
115 |
+
|
116 |
+
def on_stage_end(self, stage, stage_loss, epoch):
|
117 |
+
"""Gets called at the end of an epoch."""
|
118 |
+
# Compute/store important stats
|
119 |
+
stage_stats = {"loss": stage_loss}
|
120 |
+
if stage == sb.Stage.TRAIN:
|
121 |
+
self.train_stats = stage_stats
|
122 |
+
else:
|
123 |
+
stage_stats["CER"] = self.cer_metric.summarize("error_rate")
|
124 |
+
stage_stats["WER"] = self.wer_metric.summarize("error_rate")
|
125 |
+
|
126 |
+
# Perform end-of-iteration things, like annealing, logging, etc.
|
127 |
+
if stage == sb.Stage.VALID:
|
128 |
+
old_lr_model, new_lr_model = self.hparams.lr_annealing_model(
|
129 |
+
stage_stats["loss"]
|
130 |
+
)
|
131 |
+
old_lr_wav2vec, new_lr_wav2vec = self.hparams.lr_annealing_wav2vec(
|
132 |
+
stage_stats["loss"]
|
133 |
+
)
|
134 |
+
sb.nnet.schedulers.update_learning_rate(
|
135 |
+
self.model_optimizer, new_lr_model
|
136 |
+
)
|
137 |
+
sb.nnet.schedulers.update_learning_rate(
|
138 |
+
self.wav2vec_optimizer, new_lr_wav2vec
|
139 |
+
)
|
140 |
+
self.hparams.train_logger.log_stats(
|
141 |
+
stats_meta={
|
142 |
+
"epoch": epoch,
|
143 |
+
"lr_model": old_lr_model,
|
144 |
+
"lr_wav2vec": old_lr_wav2vec,
|
145 |
+
},
|
146 |
+
train_stats=self.train_stats,
|
147 |
+
valid_stats=stage_stats,
|
148 |
+
)
|
149 |
+
self.checkpointer.save_and_keep_only(
|
150 |
+
meta={"WER": stage_stats["WER"]}, min_keys=["WER"],
|
151 |
+
)
|
152 |
+
elif stage == sb.Stage.TEST:
|
153 |
+
self.hparams.train_logger.log_stats(
|
154 |
+
stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
|
155 |
+
test_stats=stage_stats,
|
156 |
+
)
|
157 |
+
with open(self.hparams.wer_file, "w") as w:
|
158 |
+
self.wer_metric.write_stats(w)
|
159 |
+
|
160 |
+
def init_optimizers(self):
|
161 |
+
"Initializes the wav2vec2 optimizer and model optimizer"
|
162 |
+
self.wav2vec_optimizer = self.hparams.wav2vec_opt_class(
|
163 |
+
self.modules.wav2vec2.parameters()
|
164 |
+
)
|
165 |
+
self.model_optimizer = self.hparams.model_opt_class(
|
166 |
+
self.hparams.model.parameters()
|
167 |
+
)
|
168 |
+
|
169 |
+
if self.checkpointer is not None:
|
170 |
+
self.checkpointer.add_recoverable(
|
171 |
+
"wav2vec_opt", self.wav2vec_optimizer
|
172 |
+
)
|
173 |
+
self.checkpointer.add_recoverable("modelopt", self.model_optimizer)
|
174 |
+
|
175 |
+
|
176 |
+
def dataio_prepare(hparams):
|
177 |
+
"""This function prepares the datasets to be used in the brain class.
|
178 |
+
It also defines the data processing pipeline through user-defined functions."""
|
179 |
+
data_folder = hparams["data_folder"]
|
180 |
+
|
181 |
+
train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
182 |
+
csv_path=hparams["train_csv"], replacements={"data_root": data_folder},
|
183 |
+
)
|
184 |
+
|
185 |
+
if hparams["sorting"] == "ascending":
|
186 |
+
# we sort training data to speed up training and get better results.
|
187 |
+
train_data = train_data.filtered_sorted(sort_key="duration")
|
188 |
+
# when sorting do not shuffle in dataloader ! otherwise is pointless
|
189 |
+
hparams["train_dataloader_opts"]["shuffle"] = False
|
190 |
+
|
191 |
+
elif hparams["sorting"] == "descending":
|
192 |
+
train_data = train_data.filtered_sorted(
|
193 |
+
sort_key="duration", reverse=True
|
194 |
+
)
|
195 |
+
# when sorting do not shuffle in dataloader ! otherwise is pointless
|
196 |
+
hparams["train_dataloader_opts"]["shuffle"] = False
|
197 |
+
|
198 |
+
elif hparams["sorting"] == "random":
|
199 |
+
pass
|
200 |
+
|
201 |
+
else:
|
202 |
+
raise NotImplementedError(
|
203 |
+
"sorting must be random, ascending or descending"
|
204 |
+
)
|
205 |
+
|
206 |
+
valid_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
207 |
+
csv_path=hparams["valid_csv"], replacements={"data_root": data_folder},
|
208 |
+
)
|
209 |
+
valid_data = valid_data.filtered_sorted(sort_key="duration")
|
210 |
+
|
211 |
+
# test is separate
|
212 |
+
test_datasets = {}
|
213 |
+
for csv_file in hparams["test_csv"]:
|
214 |
+
name = Path(csv_file).stem
|
215 |
+
test_datasets[name] = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
216 |
+
csv_path=csv_file, replacements={"data_root": data_folder}
|
217 |
+
)
|
218 |
+
test_datasets[name] = test_datasets[name].filtered_sorted(
|
219 |
+
sort_key="duration"
|
220 |
+
)
|
221 |
+
|
222 |
+
datasets = [train_data, valid_data] + [i for k, i in test_datasets.items()]
|
223 |
+
|
224 |
+
# 2. Define audio pipeline:
|
225 |
+
@sb.utils.data_pipeline.takes("wav", "sr")
|
226 |
+
@sb.utils.data_pipeline.provides("sig")
|
227 |
+
def audio_pipeline(wav, sr):
|
228 |
+
sig = sb.dataio.dataio.read_audio(wav)
|
229 |
+
sig = resamplers[sr](sig)
|
230 |
+
return sig
|
231 |
+
|
232 |
+
sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline)
|
233 |
+
label_encoder = sb.dataio.encoder.CTCTextEncoder()
|
234 |
+
|
235 |
+
# 3. Define text pipeline:
|
236 |
+
@sb.utils.data_pipeline.takes("wrd")
|
237 |
+
@sb.utils.data_pipeline.provides(
|
238 |
+
"wrd", "char_list", "tokens_list", "tokens_bos", "tokens_eos", "tokens"
|
239 |
+
)
|
240 |
+
def text_pipeline(wrd):
|
241 |
+
yield wrd
|
242 |
+
char_list = list(wrd)
|
243 |
+
yield char_list
|
244 |
+
tokens_list = label_encoder.encode_sequence(char_list)
|
245 |
+
yield tokens_list
|
246 |
+
tokens_bos = torch.LongTensor([hparams["bos_index"]] + (tokens_list))
|
247 |
+
yield tokens_bos
|
248 |
+
tokens_eos = torch.LongTensor(tokens_list + [hparams["eos_index"]])
|
249 |
+
yield tokens_eos
|
250 |
+
tokens = torch.LongTensor(tokens_list)
|
251 |
+
yield tokens
|
252 |
+
|
253 |
+
sb.dataio.dataset.add_dynamic_item(datasets, text_pipeline)
|
254 |
+
|
255 |
+
lab_enc_file = os.path.join(hparams["save_folder"], "label_encoder.txt")
|
256 |
+
special_labels = {
|
257 |
+
"bos_label": hparams["bos_index"],
|
258 |
+
"eos_label": hparams["eos_index"],
|
259 |
+
"blank_label": hparams["blank_index"],
|
260 |
+
}
|
261 |
+
label_encoder.load_or_create(
|
262 |
+
path=lab_enc_file,
|
263 |
+
from_didatasets=[train_data],
|
264 |
+
output_key="char_list",
|
265 |
+
special_labels=special_labels,
|
266 |
+
sequence_input=True,
|
267 |
+
)
|
268 |
+
|
269 |
+
# 4. Set output:
|
270 |
+
sb.dataio.dataset.set_output_keys(
|
271 |
+
datasets,
|
272 |
+
["id", "sig", "wrd", "char_list", "tokens_bos", "tokens_eos", "tokens"],
|
273 |
+
)
|
274 |
+
return train_data, valid_data, test_datasets, label_encoder
|
275 |
+
|
276 |
+
|
277 |
+
if __name__ == "__main__":
|
278 |
+
|
279 |
+
# CLI:
|
280 |
+
hparams_file, run_opts, overrides = sb.parse_arguments(sys.argv[1:])
|
281 |
+
|
282 |
+
# If distributed_launch=True then
|
283 |
+
# create ddp_group with the right communication protocol
|
284 |
+
sb.utils.distributed.ddp_init_group(run_opts)
|
285 |
+
|
286 |
+
with open(hparams_file) as fin:
|
287 |
+
hparams = load_hyperpyyaml(fin, overrides)
|
288 |
+
|
289 |
+
# Create experiment directory
|
290 |
+
sb.create_experiment_directory(
|
291 |
+
experiment_directory=hparams["output_folder"],
|
292 |
+
hyperparams_to_save=hparams_file,
|
293 |
+
overrides=overrides,
|
294 |
+
)
|
295 |
+
def read_labels_file(labels_file):
|
296 |
+
with open(labels_file, "r") as lf:
|
297 |
+
lines = lf.read().splitlines()
|
298 |
+
division = "==="
|
299 |
+
numbers = {}
|
300 |
+
for line in lines :
|
301 |
+
if division in line :
|
302 |
+
break
|
303 |
+
string, number = line.split("=>")
|
304 |
+
number = int(number)
|
305 |
+
string = string[1:-2]
|
306 |
+
numbers[number] = string
|
307 |
+
return [numbers[x] for x in range(len(numbers))]
|
308 |
+
labels = read_labels_file(os.path.join(hparams["save_folder"], "label_encoder.txt"))
|
309 |
+
print(labels)
|
310 |
+
labels = [""] + labels[1:]
|
311 |
+
print(len(labels))
|
312 |
+
decoder = build_ctcdecoder(
|
313 |
+
labels,
|
314 |
+
kenlm_model_path="tunisian.arpa", # either .arpa or .bin file
|
315 |
+
alpha=0.5, # tuned on a val set
|
316 |
+
beta=1.0, # tuned on a val set
|
317 |
+
)
|
318 |
+
|
319 |
+
# Dataset prep (parsing Librispeech)
|
320 |
+
|
321 |
+
resampler_8000 = T.Resample(8000, 16000, dtype=torch.float)
|
322 |
+
|
323 |
+
resampler_44100 =T.Resample(44100, 16000, dtype=torch.float)
|
324 |
+
resampler_48000 =T.Resample(48000, 16000, dtype=torch.float)
|
325 |
+
resamplers = {"8000": resampler_8000, "44100":resampler_44100, "48000": resampler_48000}
|
326 |
+
|
327 |
+
# here we create the datasets objects as well as tokenization and encoding
|
328 |
+
train_data, valid_data, test_datasets, label_encoder = dataio_prepare(
|
329 |
+
hparams
|
330 |
+
)
|
331 |
+
|
332 |
+
# Trainer initialization
|
333 |
+
asr_brain = ASR(
|
334 |
+
modules=hparams["modules"],
|
335 |
+
hparams=hparams,
|
336 |
+
run_opts=run_opts,
|
337 |
+
checkpointer=hparams["checkpointer"],
|
338 |
+
)
|
339 |
+
asr_brain.device= "cpu"
|
340 |
+
asr_brain.modules.to("cpu")
|
341 |
+
# We dynamicaly add the tokenizer to our brain class.
|
342 |
+
# NB: This tokenizer corresponds to the one used for the LM!!
|
343 |
+
asr_brain.tokenizer = label_encoder
|
344 |
+
|
345 |
+
# Training
|
346 |
+
asr_brain.fit(
|
347 |
+
asr_brain.hparams.epoch_counter,
|
348 |
+
train_data,
|
349 |
+
valid_data,
|
350 |
+
train_loader_kwargs=hparams["train_dataloader_opts"],
|
351 |
+
valid_loader_kwargs=hparams["valid_dataloader_opts"],
|
352 |
+
)
|
353 |
+
|
354 |
+
# Testing
|
355 |
+
for k in test_datasets.keys(): # keys are test_clean, test_other etc
|
356 |
+
asr_brain.hparams.wer_file = os.path.join(
|
357 |
+
hparams["output_folder"], "wer_{}.txt".format(k)
|
358 |
+
)
|
359 |
+
asr_brain.evaluate(
|
360 |
+
test_datasets[k], test_loader_kwargs=hparams["test_dataloader_opts"]
|
361 |
+
)
|
partly_frozen_splitted_wavlm/1986/log.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/CKPT.yaml
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# yamllint disable
|
2 |
+
WER: 47.68855883035507
|
3 |
+
end-of-epoch: true
|
4 |
+
unixtime: 1672916345.1685827
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/brain.ckpt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:33809a026a2c1febce7b03c8aafaee4ddfc851b2c70f180f8c06bf1017f4df5c
|
3 |
+
size 46
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/counter.ckpt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6b51d431df5d7f141cbececcf79edf3dd861c3b4069f0b11661a3eefacbba918
|
3 |
+
size 2
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/dataloader-TRAIN.ckpt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d53eb07a66864f0a18b5a0c5d029b33bddb11f05025a6e385c92c9fbb618edee
|
3 |
+
size 6
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/model.ckpt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:8782ec6abb3dd1a6aa05d54a8f159375707d2ef212f2d934c1209aab5f01a46b
|
3 |
+
size 8566935
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/modelopt.ckpt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:18828222ecebe54247f2e16e2eedf607c5fc34d0b0029f4cc3356f9397a10802
|
3 |
+
size 17133057
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/scheduler_model.ckpt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:524fb9db201619c436c0f51539869e8ffd90c8b60bf30384726052e65076751a
|
3 |
+
size 623
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/scheduler_wav2vec.ckpt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:4663a4d525410c34529e6c80671db216a35c9f285f1cf9a1ca7004d3e14679c7
|
3 |
+
size 623
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/wav2vec2.ckpt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:96716b4e96d96f3aa00182e1e5bf99219b86f74990e2f05cb69165d6e96c8b4e
|
3 |
+
size 1262004913
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+11-59-05+00/wav2vec_opt.ckpt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:c39bdda929f079de1ab70cd624900b7fda0a6a6682446ab2a25296442d01862e
|
3 |
+
size 2490235001
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/CKPT.yaml
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# yamllint disable
|
2 |
+
brain_intra_epoch_ckpt: true
|
3 |
+
end-of-epoch: false
|
4 |
+
unixtime: 1672917851.8581367
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/brain.ckpt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3bc72ed3a1d0a5dc95a83b7a139fce806d6929b8db82b1f8dd010d42556698ca
|
3 |
+
size 65
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/counter.ckpt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3fdba35f04dc8c462986c992bcf875546257113072a909c162f7e470e581e278
|
3 |
+
size 2
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/dataloader-TRAIN.ckpt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:c8228f8d57233cd4d689b31048e7ec6e2c12b409b2501ac69252bbdb65ea575d
|
3 |
+
size 5
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/model.ckpt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:316a78537d2603b0ef0030df65934944a0cc88a0865c7983aa77a081605d1d05
|
3 |
+
size 8566935
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/modelopt.ckpt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d721ac115b10592cb716b2641f80edf21549fb11c74bcf443f8213249c7fa7e8
|
3 |
+
size 17133057
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/scheduler_model.ckpt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:524fb9db201619c436c0f51539869e8ffd90c8b60bf30384726052e65076751a
|
3 |
+
size 623
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/scheduler_wav2vec.ckpt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:4663a4d525410c34529e6c80671db216a35c9f285f1cf9a1ca7004d3e14679c7
|
3 |
+
size 623
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/wav2vec2.ckpt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:8616b0fbaeb370bfae2df4d924367ae88d4a17a644b22f7050a607242f036fe5
|
3 |
+
size 1262004913
|
partly_frozen_splitted_wavlm/1986/save/CKPT+2023-01-05+12-24-11+00/wav2vec_opt.ckpt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3767ccf04ad421c4a71a167b10e0d31b064bf2f91f769ec42f9510f675dea148
|
3 |
+
size 2490235001
|
partly_frozen_splitted_wavlm/1986/save/label_encoder.txt
ADDED
@@ -0,0 +1,46 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
'م' => 38
|
2 |
+
'و' => 39
|
3 |
+
' ' => 40
|
4 |
+
'ق' => 3
|
5 |
+
'ا' => 4
|
6 |
+
'ع' => 5
|
7 |
+
'د' => 6
|
8 |
+
'ة' => 7
|
9 |
+
'ت' => 8
|
10 |
+
'ش' => 9
|
11 |
+
'ي' => 10
|
12 |
+
'ك' => 11
|
13 |
+
'ه' => 12
|
14 |
+
'ل' => 13
|
15 |
+
'ح' => 14
|
16 |
+
'ب' => 15
|
17 |
+
'ن' => 16
|
18 |
+
'ى' => 17
|
19 |
+
'ر' => 18
|
20 |
+
'ف' => 19
|
21 |
+
'إ' => 20
|
22 |
+
'س' => 21
|
23 |
+
'أ' => 22
|
24 |
+
'ض' => 23
|
25 |
+
'ص' => 24
|
26 |
+
'ط' => 25
|
27 |
+
'خ' => 26
|
28 |
+
'ج' => 27
|
29 |
+
'ظ' => 28
|
30 |
+
'ز' => 29
|
31 |
+
'آ' => 30
|
32 |
+
'ذ' => 31
|
33 |
+
'غ' => 32
|
34 |
+
'ث' => 33
|
35 |
+
'ئ' => 34
|
36 |
+
'ء' => 35
|
37 |
+
'ؤ' => 36
|
38 |
+
'ٱ' => 37
|
39 |
+
'<blank>' => 0
|
40 |
+
'<bos>' => 1
|
41 |
+
'<eos>' => 2
|
42 |
+
================
|
43 |
+
'starting_index' => 0
|
44 |
+
'bos_label' => '<bos>'
|
45 |
+
'eos_label' => '<eos>'
|
46 |
+
'blank_label' => '<blank>'
|
partly_frozen_splitted_wavlm/1986/train_log.txt
ADDED
@@ -0,0 +1,86 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
epoch: 1, lr_model: 1, lr_wav2vec: 1.00e-04 - train loss: 1.24 - valid loss: 9.24e-01, valid CER: 26.54, valid WER: 59.12
|
2 |
+
epoch: 2, lr_model: 1, lr_wav2vec: 1.00e-04 - train loss: 9.67e-01 - valid loss: 9.03e-01, valid CER: 25.85, valid WER: 57.02
|
3 |
+
epoch: 3, lr_model: 1, lr_wav2vec: 1.00e-04 - train loss: 8.84e-01 - valid loss: 8.81e-01, valid CER: 24.89, valid WER: 55.02
|
4 |
+
epoch: 4, lr_model: 1, lr_wav2vec: 1.00e-04 - train loss: 8.19e-01 - valid loss: 8.31e-01, valid CER: 22.92, valid WER: 51.69
|
5 |
+
epoch: 5, lr_model: 1, lr_wav2vec: 1.00e-04 - train loss: 7.76e-01 - valid loss: 8.67e-01, valid CER: 23.36, valid WER: 50.67
|
6 |
+
epoch: 6, lr_model: 8.00e-01, lr_wav2vec: 9.00e-05 - train loss: 7.20e-01 - valid loss: 8.37e-01, valid CER: 22.87, valid WER: 49.84
|
7 |
+
epoch: 7, lr_model: 8.00e-01, lr_wav2vec: 9.00e-05 - train loss: 6.87e-01 - valid loss: 8.78e-01, valid CER: 23.90, valid WER: 51.19
|
8 |
+
epoch: 8, lr_model: 6.40e-01, lr_wav2vec: 8.10e-05 - train loss: 6.44e-01 - valid loss: 8.68e-01, valid CER: 23.03, valid WER: 49.83
|
9 |
+
epoch: 9, lr_model: 6.40e-01, lr_wav2vec: 8.10e-05 - train loss: 6.20e-01 - valid loss: 8.47e-01, valid CER: 22.77, valid WER: 48.42
|
10 |
+
epoch: 10, lr_model: 6.40e-01, lr_wav2vec: 8.10e-05 - train loss: 5.98e-01 - valid loss: 9.07e-01, valid CER: 24.31, valid WER: 49.76
|
11 |
+
epoch: 11, lr_model: 5.12e-01, lr_wav2vec: 7.29e-05 - train loss: 5.60e-01 - valid loss: 9.08e-01, valid CER: 23.75, valid WER: 49.33
|
12 |
+
epoch: 12, lr_model: 4.10e-01, lr_wav2vec: 6.56e-05 - train loss: 5.22e-01 - valid loss: 9.08e-01, valid CER: 22.61, valid WER: 47.69
|
13 |
+
Epoch loaded: 12 - test loss: 1.26e-04, test CER: 9.09, test WER: 42.65
|
14 |
+
Epoch loaded: 12 - test loss: 5.95e-02, test CER: 20.52, test WER: 40.71
|
15 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 10.97, test WER: 54.41
|
16 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
17 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
18 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
19 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
20 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
21 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
22 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
23 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
24 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
25 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
26 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
27 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
28 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
29 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
30 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
31 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
32 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
33 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
34 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
35 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
36 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
37 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
38 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
39 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
40 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
41 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
42 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
43 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 10.03, test WER: 48.53
|
44 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 10.03, test WER: 48.53
|
45 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 10.03, test WER: 48.53
|
46 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 10.03, test WER: 48.53
|
47 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.72, test WER: 45.59
|
48 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.72, test WER: 45.59
|
49 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.72, test WER: 45.59
|
50 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.72, test WER: 45.59
|
51 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.72, test WER: 45.59
|
52 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.72, test WER: 45.59
|
53 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.72, test WER: 45.59
|
54 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.72, test WER: 45.59
|
55 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.72, test WER: 45.59
|
56 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.72, test WER: 45.59
|
57 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.40, test WER: 45.59
|
58 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.40, test WER: 45.59
|
59 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.40, test WER: 45.59
|
60 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
61 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
62 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
63 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
64 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
65 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
66 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
67 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
68 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
69 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
70 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
71 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
72 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
73 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
74 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
75 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
76 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
77 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
78 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 8.78, test WER: 44.12
|
79 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 10.66, test WER: 54.41
|
80 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 11.60, test WER: 60.29
|
81 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
82 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
83 |
+
Epoch loaded: 12 - test loss: 7.97e-04, test CER: 9.09, test WER: 42.65
|
84 |
+
Epoch loaded: 12 - test loss: 6.99e-05, test CER: 8.82, test WER: 42.86
|
85 |
+
Epoch loaded: 12 - test loss: 6.99e-05, test CER: 8.82, test WER: 42.86
|
86 |
+
Epoch loaded: 12 - test loss: 6.99e-05, test CER: 8.82, test WER: 42.86
|
partly_frozen_splitted_wavlm/1986/wer_test.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|
partly_frozen_splitted_wavlm/1986/wer_test_salah.txt
ADDED
@@ -0,0 +1,61 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
%WER 42.65 [ 29 / 68, 1 ins, 7 del, 21 sub ]
|
2 |
+
%SER 90.00 [ 9 / 10 ]
|
3 |
+
Scored 10 sentences, 0 not present in hyp.
|
4 |
+
================================================================================
|
5 |
+
ALIGNMENTS
|
6 |
+
|
7 |
+
Format:
|
8 |
+
<utterance-id>, WER DETAILS
|
9 |
+
<eps> ; reference ; on ; the ; first ; line
|
10 |
+
I ; S ; = ; = ; S ; D
|
11 |
+
and ; hypothesis ; on ; the ; third ; <eps>
|
12 |
+
================================================================================
|
13 |
+
Salah4, %WER 0.00 [ 0 / 5, 0 ins, 0 del, 0 sub ]
|
14 |
+
تعبت ; هاني ; راكش ; في ; الدار
|
15 |
+
= ; = ; = ; = ; =
|
16 |
+
تعبت ; هاني ; راكش ; في ; الدار
|
17 |
+
================================================================================
|
18 |
+
Salah5, %WER 57.14 [ 4 / 7, 0 ins, 1 del, 3 sub ]
|
19 |
+
نهار ; السبت ; ماشي ; نقرى ; ان ; شاء ; الله
|
20 |
+
= ; = ; = ; S ; S ; S ; D
|
21 |
+
نهار ; السبت ; ماشي ; نقرا ; إن ; شاءالله ; <eps>
|
22 |
+
================================================================================
|
23 |
+
Salah2, %WER 60.00 [ 3 / 5, 0 ins, 1 del, 2 sub ]
|
24 |
+
باهي ; وقتاش ; نمشيو ; ال ; تونس
|
25 |
+
= ; = ; S ; S ; D
|
26 |
+
باهي ; وقتاش ; نمشيوا ; لتونس ; <eps>
|
27 |
+
================================================================================
|
28 |
+
Salah7, %WER 33.33 [ 2 / 6, 0 ins, 1 del, 1 sub ]
|
29 |
+
نحب ; نمشي ; ال ; بنزرت ; نرتاح ; شوية
|
30 |
+
= ; = ; S ; D ; = ; =
|
31 |
+
نحب ; نمشي ; لبنزرت ; <eps> ; نرتاح ; شوية
|
32 |
+
================================================================================
|
33 |
+
Salah6, %WER 37.50 [ 3 / 8, 0 ins, 0 del, 3 sub ]
|
34 |
+
زعما ; نلقى ; أحمد ; في ; الستاد ; ولا ; ماهوش ; هوني
|
35 |
+
S ; = ; = ; = ; = ; S ; = ; S
|
36 |
+
زعمة ; نلقى ; أحمد ; في ; الستاد ; وإلا ; ماهوش ; كوني
|
37 |
+
================================================================================
|
38 |
+
Salah10, %WER 66.67 [ 4 / 6, 1 ins, 1 del, 2 sub ]
|
39 |
+
انتي ; <eps> ; خويا ; و ; عشيري ; صالح ; نحبك
|
40 |
+
S ; I ; = ; S ; D ; = ; =
|
41 |
+
إنت ; ي ; خويا ; وعشيلي ; <eps> ; صالح ; نحبك
|
42 |
+
================================================================================
|
43 |
+
Salah8, %WER 11.11 [ 1 / 9, 0 ins, 0 del, 1 sub ]
|
44 |
+
حكيت ; مع ; لولاد ; قالولي ; كل ; شي ; مريقل ; نهار ; السبت
|
45 |
+
= ; = ; S ; = ; = ; = ; = ; = ; =
|
46 |
+
حكيت ; مع ; الاولاد ; قالولي ; كل ; شي ; مريقل ; نهار ; السبت
|
47 |
+
================================================================================
|
48 |
+
Salah3, %WER 85.71 [ 6 / 7, 0 ins, 1 del, 5 sub ]
|
49 |
+
اعطيني ; خمسة ; الاف ; و ; خمسة ; ميا ; بلاهي
|
50 |
+
S ; = ; S ; S ; S ; S ; D
|
51 |
+
أعطيني ; خمسة ; آلاف ; وخمسة ; مية ; باللاهي ; <eps>
|
52 |
+
================================================================================
|
53 |
+
Salah9, %WER 37.50 [ 3 / 8, 0 ins, 1 del, 2 sub ]
|
54 |
+
ناكل ; كفتاجي ; و ; نجم ; نشري ; شوية ; حوت ; زادة
|
55 |
+
= ; S ; S ; D ; = ; = ; = ; =
|
56 |
+
ناكل ; الكفتاجي ; وننجم ; <eps> ; نشري ; شوية ; حوت ; زادة
|
57 |
+
================================================================================
|
58 |
+
Salah1, %WER 42.86 [ 3 / 7, 0 ins, 1 del, 2 sub ]
|
59 |
+
نحب ; ماكلة ; بنينة ; كسكروت ; نظيف ; و ; رخيص
|
60 |
+
S ; = ; = ; = ; = ; S ; D
|
61 |
+
لحم ; ماكلة ; بنينة ; كسكروت ; نظيف ; ورخيص ; <eps>
|
partly_frozen_splitted_wavlm/1986/wer_test_salah_local.txt
ADDED
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
%WER 42.86 [ 6 / 14, 0 ins, 2 del, 4 sub ]
|
2 |
+
%SER 100.00 [ 2 / 2 ]
|
3 |
+
Scored 2 sentences, 0 not present in hyp.
|
4 |
+
================================================================================
|
5 |
+
ALIGNMENTS
|
6 |
+
|
7 |
+
Format:
|
8 |
+
<utterance-id>, WER DETAILS
|
9 |
+
<eps> ; reference ; on ; the ; first ; line
|
10 |
+
I ; S ; = ; = ; S ; D
|
11 |
+
and ; hypothesis ; on ; the ; third ; <eps>
|
12 |
+
================================================================================
|
13 |
+
Salah1, %WER 42.86 [ 3 / 7, 0 ins, 1 del, 2 sub ]
|
14 |
+
نحب ; ماكلة ; بنينة ; كسكروت ; نظيف ; و ; رخيص
|
15 |
+
S ; = ; = ; = ; = ; S ; D
|
16 |
+
لحم ; ماكلة ; بنينة ; كسكروت ; نظيف ; ورخيص ; <eps>
|
17 |
+
================================================================================
|
18 |
+
Salah2, %WER 42.86 [ 3 / 7, 0 ins, 1 del, 2 sub ]
|
19 |
+
نحب ; ماكلة ; بنينة ; كسكروت ; نظيف ; و ; رخيص
|
20 |
+
S ; = ; = ; = ; = ; S ; D
|
21 |
+
لحم ; ماكلة ; بنينة ; كسكروت ; نظيف ; ورخيص ; <eps>
|
partly_frozen_splitted_wavlm/ctc_train.py
ADDED
@@ -0,0 +1,339 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env/python3
|
2 |
+
"""Recipe for training a wav2vec-based ctc ASR system with librispeech.
|
3 |
+
The system employs wav2vec as its encoder. Decoding is performed with
|
4 |
+
ctc greedy decoder.
|
5 |
+
To run this recipe, do the following:
|
6 |
+
> python train_with_wav2vec.py hparams/train_with_wav2vec.yaml
|
7 |
+
The neural network is trained on CTC likelihood target and character units
|
8 |
+
are used as basic recognition tokens. Training is performed on the full
|
9 |
+
LibriSpeech dataset (960 h).
|
10 |
+
|
11 |
+
Authors
|
12 |
+
* Sung-Lin Yeh 2021
|
13 |
+
* Titouan Parcollet 2021
|
14 |
+
* Ju-Chieh Chou 2020
|
15 |
+
* Mirco Ravanelli 2020
|
16 |
+
* Abdel Heba 2020
|
17 |
+
* Peter Plantinga 2020
|
18 |
+
* Samuele Cornell 2020
|
19 |
+
"""
|
20 |
+
|
21 |
+
import os
|
22 |
+
import sys
|
23 |
+
import torch
|
24 |
+
import logging
|
25 |
+
import speechbrain as sb
|
26 |
+
from speechbrain.utils.distributed import run_on_main
|
27 |
+
from hyperpyyaml import load_hyperpyyaml
|
28 |
+
from pathlib import Path
|
29 |
+
import torchaudio.transforms as T
|
30 |
+
logger = logging.getLogger(__name__)
|
31 |
+
|
32 |
+
# Define training procedure
|
33 |
+
class ASR(sb.Brain):
|
34 |
+
def compute_forward(self, batch, stage):
|
35 |
+
"""Forward computations from the waveform batches to the output probabilities."""
|
36 |
+
batch = batch.to(self.device)
|
37 |
+
wavs, wav_lens = batch.sig
|
38 |
+
tokens_bos, _ = batch.tokens_bos
|
39 |
+
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
|
40 |
+
|
41 |
+
# Forward pass
|
42 |
+
feats = self.modules.wav2vec2(wavs)
|
43 |
+
x = self.modules.enc(feats)
|
44 |
+
# Compute outputs
|
45 |
+
p_tokens = None
|
46 |
+
logits = self.modules.ctc_lin(x)
|
47 |
+
p_ctc = self.hparams.log_softmax(logits)
|
48 |
+
if stage != sb.Stage.TRAIN:
|
49 |
+
p_tokens = sb.decoders.ctc_greedy_decode(
|
50 |
+
p_ctc, wav_lens, blank_id=self.hparams.blank_index
|
51 |
+
)
|
52 |
+
return p_ctc, wav_lens, p_tokens
|
53 |
+
|
54 |
+
def compute_objectives(self, predictions, batch, stage):
|
55 |
+
"""Computes the loss (CTC+NLL) given predictions and targets."""
|
56 |
+
|
57 |
+
p_ctc, wav_lens, predicted_tokens = predictions
|
58 |
+
|
59 |
+
ids = batch.id
|
60 |
+
tokens_eos, tokens_eos_lens = batch.tokens_eos
|
61 |
+
tokens, tokens_lens = batch.tokens
|
62 |
+
|
63 |
+
if hasattr(self.modules, "env_corrupt") and stage == sb.Stage.TRAIN:
|
64 |
+
tokens_eos = torch.cat([tokens_eos, tokens_eos], dim=0)
|
65 |
+
tokens_eos_lens = torch.cat(
|
66 |
+
[tokens_eos_lens, tokens_eos_lens], dim=0
|
67 |
+
)
|
68 |
+
tokens = torch.cat([tokens, tokens], dim=0)
|
69 |
+
tokens_lens = torch.cat([tokens_lens, tokens_lens], dim=0)
|
70 |
+
|
71 |
+
loss_ctc = self.hparams.ctc_cost(p_ctc, tokens, wav_lens, tokens_lens)
|
72 |
+
loss = loss_ctc
|
73 |
+
|
74 |
+
if stage != sb.Stage.TRAIN:
|
75 |
+
# Decode token terms to words
|
76 |
+
predicted_words = [
|
77 |
+
"".join(self.tokenizer.decode_ndim(utt_seq)).split(" ")
|
78 |
+
for utt_seq in predicted_tokens
|
79 |
+
]
|
80 |
+
target_words = [wrd.split(" ") for wrd in batch.wrd]
|
81 |
+
self.wer_metric.append(ids, predicted_words, target_words)
|
82 |
+
self.cer_metric.append(ids, predicted_words, target_words)
|
83 |
+
|
84 |
+
return loss
|
85 |
+
|
86 |
+
def fit_batch(self, batch):
|
87 |
+
"""Train the parameters given a single batch in input"""
|
88 |
+
predictions = self.compute_forward(batch, sb.Stage.TRAIN)
|
89 |
+
loss = self.compute_objectives(predictions, batch, sb.Stage.TRAIN)
|
90 |
+
loss.backward()
|
91 |
+
if self.check_gradients(loss):
|
92 |
+
self.wav2vec_optimizer.step()
|
93 |
+
self.model_optimizer.step()
|
94 |
+
|
95 |
+
self.wav2vec_optimizer.zero_grad()
|
96 |
+
self.model_optimizer.zero_grad()
|
97 |
+
|
98 |
+
return loss.detach()
|
99 |
+
|
100 |
+
def evaluate_batch(self, batch, stage):
|
101 |
+
"""Computations needed for validation/test batches"""
|
102 |
+
predictions = self.compute_forward(batch, stage=stage)
|
103 |
+
with torch.no_grad():
|
104 |
+
loss = self.compute_objectives(predictions, batch, stage=stage)
|
105 |
+
return loss.detach()
|
106 |
+
|
107 |
+
def on_stage_start(self, stage, epoch):
|
108 |
+
"""Gets called at the beginning of each epoch"""
|
109 |
+
if stage != sb.Stage.TRAIN:
|
110 |
+
self.cer_metric = self.hparams.cer_computer()
|
111 |
+
self.wer_metric = self.hparams.error_rate_computer()
|
112 |
+
|
113 |
+
def on_stage_end(self, stage, stage_loss, epoch):
|
114 |
+
"""Gets called at the end of an epoch."""
|
115 |
+
# Compute/store important stats
|
116 |
+
stage_stats = {"loss": stage_loss}
|
117 |
+
if stage == sb.Stage.TRAIN:
|
118 |
+
self.train_stats = stage_stats
|
119 |
+
else:
|
120 |
+
stage_stats["CER"] = self.cer_metric.summarize("error_rate")
|
121 |
+
stage_stats["WER"] = self.wer_metric.summarize("error_rate")
|
122 |
+
|
123 |
+
# Perform end-of-iteration things, like annealing, logging, etc.
|
124 |
+
if stage == sb.Stage.VALID:
|
125 |
+
old_lr_model, new_lr_model = self.hparams.lr_annealing_model(
|
126 |
+
stage_stats["loss"]
|
127 |
+
)
|
128 |
+
old_lr_wav2vec, new_lr_wav2vec = self.hparams.lr_annealing_wav2vec(
|
129 |
+
stage_stats["loss"]
|
130 |
+
)
|
131 |
+
sb.nnet.schedulers.update_learning_rate(
|
132 |
+
self.model_optimizer, new_lr_model
|
133 |
+
)
|
134 |
+
sb.nnet.schedulers.update_learning_rate(
|
135 |
+
self.wav2vec_optimizer, new_lr_wav2vec
|
136 |
+
)
|
137 |
+
self.hparams.train_logger.log_stats(
|
138 |
+
stats_meta={
|
139 |
+
"epoch": epoch,
|
140 |
+
"lr_model": old_lr_model,
|
141 |
+
"lr_wav2vec": old_lr_wav2vec,
|
142 |
+
},
|
143 |
+
train_stats=self.train_stats,
|
144 |
+
valid_stats=stage_stats,
|
145 |
+
)
|
146 |
+
self.checkpointer.save_and_keep_only(
|
147 |
+
meta={"WER": stage_stats["WER"]}, min_keys=["WER"],
|
148 |
+
)
|
149 |
+
elif stage == sb.Stage.TEST:
|
150 |
+
self.hparams.train_logger.log_stats(
|
151 |
+
stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
|
152 |
+
test_stats=stage_stats,
|
153 |
+
)
|
154 |
+
with open(self.hparams.wer_file, "w") as w:
|
155 |
+
self.wer_metric.write_stats(w)
|
156 |
+
|
157 |
+
def init_optimizers(self):
|
158 |
+
"Initializes the wav2vec2 optimizer and model optimizer"
|
159 |
+
self.wav2vec_optimizer = self.hparams.wav2vec_opt_class(
|
160 |
+
self.modules.wav2vec2.parameters()
|
161 |
+
)
|
162 |
+
self.model_optimizer = self.hparams.model_opt_class(
|
163 |
+
self.hparams.model.parameters()
|
164 |
+
)
|
165 |
+
|
166 |
+
if self.checkpointer is not None:
|
167 |
+
self.checkpointer.add_recoverable(
|
168 |
+
"wav2vec_opt", self.wav2vec_optimizer
|
169 |
+
)
|
170 |
+
self.checkpointer.add_recoverable("modelopt", self.model_optimizer)
|
171 |
+
|
172 |
+
|
173 |
+
def dataio_prepare(hparams):
|
174 |
+
"""This function prepares the datasets to be used in the brain class.
|
175 |
+
It also defines the data processing pipeline through user-defined functions."""
|
176 |
+
data_folder = hparams["data_folder"]
|
177 |
+
|
178 |
+
train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
179 |
+
csv_path=hparams["train_csv"], replacements={"data_root": data_folder},
|
180 |
+
)
|
181 |
+
|
182 |
+
if hparams["sorting"] == "ascending":
|
183 |
+
# we sort training data to speed up training and get better results.
|
184 |
+
train_data = train_data.filtered_sorted(sort_key="duration")
|
185 |
+
# when sorting do not shuffle in dataloader ! otherwise is pointless
|
186 |
+
hparams["train_dataloader_opts"]["shuffle"] = False
|
187 |
+
|
188 |
+
elif hparams["sorting"] == "descending":
|
189 |
+
train_data = train_data.filtered_sorted(
|
190 |
+
sort_key="duration", reverse=True
|
191 |
+
)
|
192 |
+
# when sorting do not shuffle in dataloader ! otherwise is pointless
|
193 |
+
hparams["train_dataloader_opts"]["shuffle"] = False
|
194 |
+
|
195 |
+
elif hparams["sorting"] == "random":
|
196 |
+
pass
|
197 |
+
|
198 |
+
else:
|
199 |
+
raise NotImplementedError(
|
200 |
+
"sorting must be random, ascending or descending"
|
201 |
+
)
|
202 |
+
|
203 |
+
valid_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
204 |
+
csv_path=hparams["valid_csv"], replacements={"data_root": data_folder},
|
205 |
+
)
|
206 |
+
valid_data = valid_data.filtered_sorted(sort_key="duration")
|
207 |
+
|
208 |
+
# test is separate
|
209 |
+
test_datasets = {}
|
210 |
+
for csv_file in hparams["test_csv"]:
|
211 |
+
name = Path(csv_file).stem
|
212 |
+
test_datasets[name] = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
213 |
+
csv_path=csv_file, replacements={"data_root": data_folder}
|
214 |
+
)
|
215 |
+
test_datasets[name] = test_datasets[name].filtered_sorted(
|
216 |
+
sort_key="duration"
|
217 |
+
)
|
218 |
+
|
219 |
+
datasets = [train_data, valid_data] + [i for k, i in test_datasets.items()]
|
220 |
+
|
221 |
+
# 2. Define audio pipeline:
|
222 |
+
@sb.utils.data_pipeline.takes("wav", "sr")
|
223 |
+
@sb.utils.data_pipeline.provides("sig")
|
224 |
+
def audio_pipeline(wav, sr):
|
225 |
+
sig = sb.dataio.dataio.read_audio(wav)
|
226 |
+
sig = resamplers[sr](sig)
|
227 |
+
return sig
|
228 |
+
|
229 |
+
sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline)
|
230 |
+
label_encoder = sb.dataio.encoder.CTCTextEncoder()
|
231 |
+
|
232 |
+
# 3. Define text pipeline:
|
233 |
+
@sb.utils.data_pipeline.takes("wrd")
|
234 |
+
@sb.utils.data_pipeline.provides(
|
235 |
+
"wrd", "char_list", "tokens_list", "tokens_bos", "tokens_eos", "tokens"
|
236 |
+
)
|
237 |
+
def text_pipeline(wrd):
|
238 |
+
yield wrd
|
239 |
+
char_list = list(wrd)
|
240 |
+
yield char_list
|
241 |
+
tokens_list = label_encoder.encode_sequence(char_list)
|
242 |
+
yield tokens_list
|
243 |
+
tokens_bos = torch.LongTensor([hparams["bos_index"]] + (tokens_list))
|
244 |
+
yield tokens_bos
|
245 |
+
tokens_eos = torch.LongTensor(tokens_list + [hparams["eos_index"]])
|
246 |
+
yield tokens_eos
|
247 |
+
tokens = torch.LongTensor(tokens_list)
|
248 |
+
yield tokens
|
249 |
+
|
250 |
+
sb.dataio.dataset.add_dynamic_item(datasets, text_pipeline)
|
251 |
+
|
252 |
+
lab_enc_file = os.path.join(hparams["save_folder"], "label_encoder.txt")
|
253 |
+
special_labels = {
|
254 |
+
"bos_label": hparams["bos_index"],
|
255 |
+
"eos_label": hparams["eos_index"],
|
256 |
+
"blank_label": hparams["blank_index"],
|
257 |
+
}
|
258 |
+
label_encoder.load_or_create(
|
259 |
+
path=lab_enc_file,
|
260 |
+
from_didatasets=[train_data],
|
261 |
+
output_key="char_list",
|
262 |
+
special_labels=special_labels,
|
263 |
+
sequence_input=True,
|
264 |
+
)
|
265 |
+
|
266 |
+
# 4. Set output:
|
267 |
+
sb.dataio.dataset.set_output_keys(
|
268 |
+
datasets,
|
269 |
+
["id", "sig", "wrd", "char_list", "tokens_bos", "tokens_eos", "tokens"],
|
270 |
+
)
|
271 |
+
return train_data, valid_data, test_datasets, label_encoder
|
272 |
+
|
273 |
+
|
274 |
+
if __name__ == "__main__":
|
275 |
+
|
276 |
+
# CLI:
|
277 |
+
hparams_file, run_opts, overrides = sb.parse_arguments(sys.argv[1:])
|
278 |
+
|
279 |
+
# If distributed_launch=True then
|
280 |
+
# create ddp_group with the right communication protocol
|
281 |
+
sb.utils.distributed.ddp_init_group(run_opts)
|
282 |
+
|
283 |
+
with open(hparams_file) as fin:
|
284 |
+
hparams = load_hyperpyyaml(fin, overrides)
|
285 |
+
|
286 |
+
# Create experiment directory
|
287 |
+
sb.create_experiment_directory(
|
288 |
+
experiment_directory=hparams["output_folder"],
|
289 |
+
hyperparams_to_save=hparams_file,
|
290 |
+
overrides=overrides,
|
291 |
+
)
|
292 |
+
|
293 |
+
# Dataset prep (parsing Librispeech)
|
294 |
+
|
295 |
+
resampler_8000 = T.Resample(8000, 16000, dtype=torch.float)
|
296 |
+
|
297 |
+
resampler_44100 =T.Resample(44100, 16000, dtype=torch.float)
|
298 |
+
resampler_32000 =T.Resample(32000, 16000, dtype=torch.float)
|
299 |
+
resampler_48000 =T.Resample(48000, 16000, dtype=torch.float)
|
300 |
+
|
301 |
+
|
302 |
+
resamplers = {"48000": resampler_48000,"8000": resampler_8000, "44100":resampler_44100, "32000":resampler_32000}
|
303 |
+
|
304 |
+
# here we create the datasets objects as well as tokenization and encoding
|
305 |
+
train_data, valid_data, test_datasets, label_encoder = dataio_prepare(
|
306 |
+
hparams
|
307 |
+
)
|
308 |
+
|
309 |
+
# Trainer initialization
|
310 |
+
asr_brain = ASR(
|
311 |
+
modules=hparams["modules"],
|
312 |
+
hparams=hparams,
|
313 |
+
run_opts=run_opts,
|
314 |
+
checkpointer=hparams["checkpointer"],
|
315 |
+
)
|
316 |
+
asr_brain.device= "cpu"
|
317 |
+
asr_brain.modules.to("cpu")
|
318 |
+
|
319 |
+
# We dynamicaly add the tokenizer to our brain class.
|
320 |
+
# NB: This tokenizer corresponds to the one used for the LM!!
|
321 |
+
asr_brain.tokenizer = label_encoder
|
322 |
+
|
323 |
+
# Training
|
324 |
+
asr_brain.fit(
|
325 |
+
asr_brain.hparams.epoch_counter,
|
326 |
+
train_data,
|
327 |
+
valid_data,
|
328 |
+
train_loader_kwargs=hparams["train_dataloader_opts"],
|
329 |
+
valid_loader_kwargs=hparams["valid_dataloader_opts"],
|
330 |
+
)
|
331 |
+
|
332 |
+
# Testing
|
333 |
+
for k in test_datasets.keys(): # keys are test_clean, test_other etc
|
334 |
+
asr_brain.hparams.wer_file = os.path.join(
|
335 |
+
hparams["output_folder"], "wer_{}.txt".format(k)
|
336 |
+
)
|
337 |
+
asr_brain.evaluate(
|
338 |
+
test_datasets[k], test_loader_kwargs=hparams["test_dataloader_opts"]
|
339 |
+
)
|
partly_frozen_splitted_wavlm/env.log
ADDED
@@ -0,0 +1,379 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
SpeechBrain system description
|
2 |
+
==============================
|
3 |
+
Python version:
|
4 |
+
3.8.5 (default, Sep 4 2020, 07:30:14)
|
5 |
+
[GCC 7.3.0]
|
6 |
+
==============================
|
7 |
+
Installed Python packages:
|
8 |
+
abkhazia==1.0
|
9 |
+
absl-py==0.11.0
|
10 |
+
aiohttp==3.8.0
|
11 |
+
aiosignal==1.2.0
|
12 |
+
alabaster==0.7.12
|
13 |
+
alembic==1.7.4
|
14 |
+
altgraph==0.17
|
15 |
+
antlr4-python3-runtime==4.8
|
16 |
+
appdirs==1.4.4
|
17 |
+
argcomplete==1.12.2
|
18 |
+
argon2-cffi==20.1.0
|
19 |
+
asgiref==3.6.0
|
20 |
+
astunparse==1.6.3
|
21 |
+
async-generator==1.10
|
22 |
+
async-timeout==4.0.0
|
23 |
+
attrdict==2.0.1
|
24 |
+
attrs==20.3.0
|
25 |
+
audeer==1.16.0
|
26 |
+
audformat==0.11.5
|
27 |
+
audinterface==0.7.0
|
28 |
+
audiofile==1.0.0
|
29 |
+
audiomentations==0.25.0
|
30 |
+
audioread==2.1.9
|
31 |
+
audobject==0.4.14
|
32 |
+
audresample==0.1.6
|
33 |
+
-e git+https://github.com/facebookresearch/WavAugment.git@54afcdb00ccc852c2f030f239f8532c9562b550e#egg=augment
|
34 |
+
autopage==0.4.0
|
35 |
+
Babel==2.9.0
|
36 |
+
backcall==0.2.0
|
37 |
+
beautifulsoup4==4.10.0
|
38 |
+
black==19.10b0
|
39 |
+
bleach==3.3.0
|
40 |
+
boto3==1.20.2
|
41 |
+
botocore==1.23.2
|
42 |
+
braceexpand==0.1.7
|
43 |
+
cachetools==4.2.0
|
44 |
+
certifi @ file:///croot/certifi_1671487769961/work/certifi
|
45 |
+
cffi==1.14.3
|
46 |
+
cfgv==3.2.0
|
47 |
+
chardet==3.0.4
|
48 |
+
charset-normalizer==2.0.7
|
49 |
+
click==7.1.2
|
50 |
+
cliff==3.9.0
|
51 |
+
clldutils==3.5.4
|
52 |
+
cmaes==0.8.2
|
53 |
+
cmake==3.18.4.post1
|
54 |
+
cmd2==2.2.0
|
55 |
+
colorama==0.4.4
|
56 |
+
colorlog==4.6.2
|
57 |
+
configparser==5.1.0
|
58 |
+
cryptography==38.0.4
|
59 |
+
csvw==1.8.1
|
60 |
+
cycler==0.10.0
|
61 |
+
Cython==0.29.21
|
62 |
+
dataclasses==0.6
|
63 |
+
datasets==1.5.0
|
64 |
+
decorator==4.4.2
|
65 |
+
deepspeech==0.9.1
|
66 |
+
defusedxml==0.7.1
|
67 |
+
denoiser==0.1.5
|
68 |
+
dill==0.3.3
|
69 |
+
Distance==0.1.3
|
70 |
+
distlib==0.3.1
|
71 |
+
Django==3.2.16
|
72 |
+
django-auditlog==2.2.1
|
73 |
+
django-filter==22.1
|
74 |
+
django-js-asset==1.2.2
|
75 |
+
django-mptt==0.14.0
|
76 |
+
djangorestframework==3.14.0
|
77 |
+
docker-pycreds==0.4.0
|
78 |
+
docopt==0.6.2
|
79 |
+
docutils==0.16
|
80 |
+
drf-excel==2.2.0
|
81 |
+
drf-flex-fields==1.0.0
|
82 |
+
drf-renderer-xlsx==0.4.1
|
83 |
+
easyocr==1.2.1
|
84 |
+
editdistance==0.6.0
|
85 |
+
emoji==2.2.0
|
86 |
+
entrypoints==0.3
|
87 |
+
et-xmlfile==1.1.0
|
88 |
+
exceptiongroup==1.1.0
|
89 |
+
farasapy==0.0.14
|
90 |
+
fasttext==0.9.2
|
91 |
+
ffmpeg-python==0.2.0
|
92 |
+
filelock==3.0.12
|
93 |
+
flake8==3.7.9
|
94 |
+
flatbuffers==1.12
|
95 |
+
frozendict==2.0.7
|
96 |
+
frozenlist==1.2.0
|
97 |
+
fsspec==2021.11.0
|
98 |
+
future==0.18.2
|
99 |
+
g2p-en==2.1.0
|
100 |
+
gast==0.3.3
|
101 |
+
gdown==4.2.0
|
102 |
+
gensim==4.0.1
|
103 |
+
gitdb==4.0.9
|
104 |
+
GitPython==3.1.24
|
105 |
+
google-auth==1.24.0
|
106 |
+
google-auth-oauthlib==0.4.2
|
107 |
+
google-pasta==0.2.0
|
108 |
+
greenlet==1.1.2
|
109 |
+
grpcio==1.32.0
|
110 |
+
h5features==1.3.2
|
111 |
+
h5py==2.10.0
|
112 |
+
htk-io==0.5
|
113 |
+
huggingface-hub==0.9.1
|
114 |
+
hydra-colorlog==0.1.4
|
115 |
+
hydra-core==0.11.3
|
116 |
+
HyperPyYAML==1.1.0
|
117 |
+
hypothesis==6.61.2
|
118 |
+
identify==1.5.10
|
119 |
+
idna==2.10
|
120 |
+
imageio==2.9.0
|
121 |
+
imagesize==1.2.0
|
122 |
+
importlib-metadata==4.8.1
|
123 |
+
importlib-resources==5.2.2
|
124 |
+
inflect==5.3.0
|
125 |
+
ipadic==1.0.0
|
126 |
+
ipykernel==5.3.4
|
127 |
+
ipython==7.19.0
|
128 |
+
ipython-genutils==0.2.0
|
129 |
+
ipywidgets==7.6.3
|
130 |
+
iso-639==0.4.5
|
131 |
+
isodate==0.6.0
|
132 |
+
isort==4.3.21
|
133 |
+
jedi==0.17.2
|
134 |
+
jieba==0.42.1
|
135 |
+
Jinja2==2.11.2
|
136 |
+
jiwer==2.2.0
|
137 |
+
jmespath==0.10.0
|
138 |
+
joblib==0.17.0
|
139 |
+
jsonschema==3.2.0
|
140 |
+
julius==0.2.7
|
141 |
+
jupyter-client==6.1.7
|
142 |
+
jupyter-core==4.7.0
|
143 |
+
jupyterlab-pygments==0.1.2
|
144 |
+
jupyterlab-widgets==1.0.0
|
145 |
+
kaitaistruct==0.9
|
146 |
+
kaldi-io==0.9.4
|
147 |
+
kaldi-python-io==1.2.2
|
148 |
+
kaldiio==2.17.2
|
149 |
+
kenlm @ https://github.com/kpu/kenlm/archive/master.zip
|
150 |
+
Keras-Preprocessing==1.1.2
|
151 |
+
kiwisolver==1.3.1
|
152 |
+
lang-trans==0.6.0
|
153 |
+
latexcodec==2.0.1
|
154 |
+
ldap3==2.9.1
|
155 |
+
librosa==0.9.0
|
156 |
+
llvmlite==0.35.0
|
157 |
+
lxml==4.9.0
|
158 |
+
Mako==1.1.5
|
159 |
+
Markdown==3.3.3
|
160 |
+
MarkupSafe==1.1.1
|
161 |
+
marshmallow==3.14.0
|
162 |
+
matplotlib==3.3.3
|
163 |
+
mccabe==0.6.1
|
164 |
+
mcd==0.4
|
165 |
+
mecab-python3==1.0.3
|
166 |
+
megatron-lm==2.2.0
|
167 |
+
mido==1.2.10
|
168 |
+
mistune==0.8.4
|
169 |
+
more-itertools==8.6.0
|
170 |
+
mpmath==1.2.1
|
171 |
+
multidict==5.2.0
|
172 |
+
multiprocess==0.70.11.1
|
173 |
+
nbclient==0.5.3
|
174 |
+
nbconvert==6.0.7
|
175 |
+
nbformat==5.1.3
|
176 |
+
NEMO==4.3.2
|
177 |
+
nemo-toolkit==1.4.0
|
178 |
+
nest-asyncio==1.5.1
|
179 |
+
networkx==2.5
|
180 |
+
nltk==3.5
|
181 |
+
nodeenv==1.5.0
|
182 |
+
notebook==6.3.0
|
183 |
+
numba==0.52.0
|
184 |
+
numpy==1.19.4
|
185 |
+
nvidia-cublas-cu11==11.10.3.66
|
186 |
+
nvidia-cuda-nvrtc-cu11==11.7.99
|
187 |
+
nvidia-cuda-runtime-cu11==11.7.99
|
188 |
+
nvidia-cudnn-cu11==8.5.0.96
|
189 |
+
oauthlib==3.1.0
|
190 |
+
omegaconf==1.4.1
|
191 |
+
onnx==1.10.2
|
192 |
+
OpenCC==1.1.2
|
193 |
+
opencv-python==4.4.0.46
|
194 |
+
openpyxl==3.0.9
|
195 |
+
opensmile==2.2.0
|
196 |
+
opt-einsum==3.3.0
|
197 |
+
optuna==2.10.0
|
198 |
+
oyaml==1.0
|
199 |
+
packaging==22.0
|
200 |
+
pandas==1.2.5
|
201 |
+
pandocfilters==1.4.3
|
202 |
+
pangu==4.0.6.1
|
203 |
+
parameterized==0.8.1
|
204 |
+
parso==0.7.1
|
205 |
+
pathspec==0.8.1
|
206 |
+
pathtools==0.1.2
|
207 |
+
pbr==5.6.0
|
208 |
+
pefile==2019.4.18
|
209 |
+
pescador==2.1.0
|
210 |
+
pesq==0.0.3
|
211 |
+
pexpect==4.8.0
|
212 |
+
phonemizer==2.2.1
|
213 |
+
pickleshare==0.7.5
|
214 |
+
Pillow==9.3.0
|
215 |
+
pip-api==0.0.23
|
216 |
+
pipreqs==0.4.11
|
217 |
+
pluggy==0.13.1
|
218 |
+
pooch==1.3.0
|
219 |
+
portalocker==2.3.2
|
220 |
+
pre-commit==2.9.0
|
221 |
+
pretty-midi==0.2.9
|
222 |
+
prettytable==2.2.1
|
223 |
+
progressbar2==3.53.1
|
224 |
+
prometheus-client==0.10.1
|
225 |
+
promise==2.3
|
226 |
+
prompt-toolkit==3.0.8
|
227 |
+
protobuf==3.14.0
|
228 |
+
psutil==5.6.6
|
229 |
+
ptyprocess==0.6.0
|
230 |
+
py==1.9.0
|
231 |
+
py-espeak-ng==0.1.8
|
232 |
+
pyannote.audio==1.1.1
|
233 |
+
pyannote.core==4.3
|
234 |
+
pyannote.database==4.1.1
|
235 |
+
pyannote.metrics==3.1
|
236 |
+
pyannote.pipeline==1.5.2
|
237 |
+
PyArabic==0.6.15
|
238 |
+
pyarrow==3.0.0
|
239 |
+
pyasn1==0.4.8
|
240 |
+
pyasn1-modules==0.2.8
|
241 |
+
pybind11==2.8.1
|
242 |
+
pybtex==0.24.0
|
243 |
+
pybtex-docutils==1.0.1
|
244 |
+
pycodestyle==2.5.0
|
245 |
+
pycparser==2.20
|
246 |
+
pyctcdecode==0.4.0
|
247 |
+
pyDeprecate==0.3.1
|
248 |
+
pydub==0.25.1
|
249 |
+
pyflakes==2.1.1
|
250 |
+
Pygments==2.7.2
|
251 |
+
pygtrie==2.5.0
|
252 |
+
pymodbus==2.5.3
|
253 |
+
pyparsing==2.4.7
|
254 |
+
pyperclip==1.8.2
|
255 |
+
pypinyin==0.43.0
|
256 |
+
pyrsistent==0.17.3
|
257 |
+
pyserial==3.5
|
258 |
+
PySocks==1.7.1
|
259 |
+
pystoi==0.3.3
|
260 |
+
pytest==5.4.1
|
261 |
+
pytest-runner==5.3.1
|
262 |
+
python-bidi==0.4.2
|
263 |
+
python-crfsuite==0.9.7
|
264 |
+
python-dateutil==2.8.2
|
265 |
+
python-Levenshtein==0.12.2
|
266 |
+
python-utils==2.4.0
|
267 |
+
pytorch-lightning==1.4.9
|
268 |
+
pytube==11.0.1
|
269 |
+
pytz==2022.6
|
270 |
+
PyWavelets==1.1.1
|
271 |
+
PyYAML==5.3.1
|
272 |
+
pyzmq==20.0.0
|
273 |
+
rapidfuzz==1.8.2
|
274 |
+
regex==2020.11.13
|
275 |
+
requests==2.28.1
|
276 |
+
requests-oauthlib==1.3.0
|
277 |
+
resampy==0.2.2
|
278 |
+
rfc3986==1.4.0
|
279 |
+
rsa==4.7
|
280 |
+
ruamel.yaml==0.17.21
|
281 |
+
ruamel.yaml.clib==0.2.7
|
282 |
+
s3m==1.1.0
|
283 |
+
s3transfer==0.5.0
|
284 |
+
sacrebleu==2.0.0
|
285 |
+
sacremoses==0.0.44
|
286 |
+
scikit-image==0.18.1
|
287 |
+
scikit-learn==0.23.2
|
288 |
+
scipy==1.5.4
|
289 |
+
-e git+https://github.com/sanghack81/SDCIT@00d060dde733fde9345154a494f81e97fb395ca7#egg=SDCIT
|
290 |
+
seaborn==0.11.1
|
291 |
+
segments==2.1.3
|
292 |
+
Send2Trash==1.5.0
|
293 |
+
sentencepiece==0.1.94
|
294 |
+
sentry-sdk==1.4.3
|
295 |
+
shellingham==1.4.0
|
296 |
+
shortuuid==1.0.7
|
297 |
+
SIDEKIT==1.3.8.5.2
|
298 |
+
simplejson==3.17.5
|
299 |
+
six==1.15.0
|
300 |
+
smart-open==5.0.0
|
301 |
+
smmap==5.0.0
|
302 |
+
snowballstemmer==2.0.0
|
303 |
+
sortedcollections==2.1.0
|
304 |
+
sortedcontainers==2.4.0
|
305 |
+
sounddevice==0.4.5
|
306 |
+
SoundFile==0.10.3.post1
|
307 |
+
soupsieve==2.3
|
308 |
+
sox==1.4.1
|
309 |
+
sparsemax==0.1.9
|
310 |
+
speechbrain==0.5.13
|
311 |
+
sphfile==1.0.3
|
312 |
+
Sphinx==3.3.1
|
313 |
+
sphinx-rtd-theme==0.4.3
|
314 |
+
sphinxcontrib-applehelp==1.0.2
|
315 |
+
sphinxcontrib-bibtex==2.4.1
|
316 |
+
sphinxcontrib-devhelp==1.0.2
|
317 |
+
sphinxcontrib-htmlhelp==1.0.3
|
318 |
+
sphinxcontrib-jsmath==1.0.1
|
319 |
+
sphinxcontrib-qthelp==1.0.3
|
320 |
+
sphinxcontrib-serializinghtml==1.1.4
|
321 |
+
SQLAlchemy==1.4.25
|
322 |
+
sqlparse==0.4.2
|
323 |
+
stanza==1.4.2
|
324 |
+
stevedore==3.4.0
|
325 |
+
subprocess32==3.5.4
|
326 |
+
sympy==1.9
|
327 |
+
tabulate==0.8.9
|
328 |
+
tensorboard==2.4.0
|
329 |
+
tensorboard-plugin-wit==1.7.0
|
330 |
+
tensorflow==2.4.0
|
331 |
+
tensorflow-estimator==2.4.0
|
332 |
+
termcolor==1.1.0
|
333 |
+
terminado==0.9.4
|
334 |
+
testpath==0.4.4
|
335 |
+
threadpoolctl==2.1.0
|
336 |
+
tifffile==2020.12.8
|
337 |
+
tikzplotlib==0.9.8
|
338 |
+
tkseem==0.0.3
|
339 |
+
tokenizers==0.10.2
|
340 |
+
toml==0.10.2
|
341 |
+
torch==1.13.1
|
342 |
+
torch-stft==0.1.4
|
343 |
+
torchaudio==0.13.1
|
344 |
+
torchmetrics==0.6.0
|
345 |
+
torchvision==0.14.1
|
346 |
+
tornado==6.1
|
347 |
+
tqdm==4.61.1
|
348 |
+
trackrip==1.2.1
|
349 |
+
traitlets==5.0.5
|
350 |
+
transformers==4.15.0
|
351 |
+
typed-ast==1.4.1
|
352 |
+
typer==0.4.0
|
353 |
+
typing-extensions==3.7.4.3
|
354 |
+
Unidecode==1.3.2
|
355 |
+
uritemplate==3.0.1
|
356 |
+
urllib3==1.26.2
|
357 |
+
virtualenv==20.2.1
|
358 |
+
wandb==0.12.6
|
359 |
+
wcwidth==0.2.5
|
360 |
+
webdataset==0.1.62
|
361 |
+
webencodings==0.5.1
|
362 |
+
Werkzeug==1.0.1
|
363 |
+
wget==3.2
|
364 |
+
widgetsnbextension==3.5.1
|
365 |
+
wordninja==2.0.0
|
366 |
+
wrapt==1.12.1
|
367 |
+
xmltodict==0.13.0
|
368 |
+
xxhash==2.0.0
|
369 |
+
yamllint==1.23.0
|
370 |
+
yarg==0.1.9
|
371 |
+
yarl==1.7.2
|
372 |
+
yaspin==2.1.0
|
373 |
+
youtokentome==1.0.6
|
374 |
+
youtube-dl==2021.6.6
|
375 |
+
zipp==3.6.0
|
376 |
+
==============================
|
377 |
+
Could not get git revision==============================
|
378 |
+
CUDA version:
|
379 |
+
11.7
|
partly_frozen_splitted_wavlm/hyperparams.yaml
ADDED
@@ -0,0 +1,162 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Generated 2023-01-07 from:
|
2 |
+
# /home/salah/kenlm_train/to_copy/wavlm_partly_frozen.yaml
|
3 |
+
# yamllint disable
|
4 |
+
# ################################
|
5 |
+
# Model: wav2vec2 + DNN + CTC
|
6 |
+
# Augmentation: SpecAugment
|
7 |
+
# Authors: Sung-Lin Yeh 2021
|
8 |
+
# ################################
|
9 |
+
|
10 |
+
# Seed needs to be set at top of yaml, before objects with parameters are made
|
11 |
+
seed: 1986
|
12 |
+
__set_seed: !apply:torch.manual_seed [1986]
|
13 |
+
output_folder: partly_frozen_splitted_wavlm
|
14 |
+
wer_file: partly_frozen_splitted_wavlm/wer.txt
|
15 |
+
save_folder: partly_frozen_splitted_wavlm/save
|
16 |
+
train_log: partly_frozen_splitted_wavlm/train_log.txt
|
17 |
+
|
18 |
+
# URL for the biggest Fairseq english wav2vec2 model.
|
19 |
+
|
20 |
+
# Data files
|
21 |
+
data_folder: /gpfsscratch/rech/nou/uzn19yk/Libri/LibriSpeech/ # e,g./path/to/LibriSpeech
|
22 |
+
# noise/ris dataset will automatically be downloaded
|
23 |
+
data_folder_rirs: /gpfsscratch/rech/nou/uzn19yk/Libri/LibriSpeech/
|
24 |
+
train_splits: [train-clean-100]
|
25 |
+
dev_splits: [dev-clean]
|
26 |
+
test_splits: [test-clean, test-other]
|
27 |
+
skip_prep: false
|
28 |
+
ckpt_interval_minutes: 25 # save checkpoint every N min
|
29 |
+
csv_folder: /gpfsstore/rech/nou/uzn19yk/iwslt/splitted_clean_tunisian_csvs/
|
30 |
+
train_csv: test_salah_local.csv
|
31 |
+
valid_csv: test_salah_local.csv
|
32 |
+
test_csv:
|
33 |
+
- test_salah_local.csv
|
34 |
+
|
35 |
+
# Training parameters
|
36 |
+
number_of_epochs: 12
|
37 |
+
lr: 1
|
38 |
+
lr_wav2vec: 0.0001
|
39 |
+
sorting: ascending
|
40 |
+
auto_mix_prec: false
|
41 |
+
sample_rate: 16000
|
42 |
+
|
43 |
+
avoid_if_longer_than: 10
|
44 |
+
# With data_parallel batch_size is split into N jobs
|
45 |
+
# With DDP batch_size is multiplied by N jobs
|
46 |
+
# Must be 3 per GPU to fit 32GB of VRAM
|
47 |
+
batch_size: 1
|
48 |
+
test_batch_size: 1
|
49 |
+
|
50 |
+
# Dataloader options
|
51 |
+
train_dataloader_opts:
|
52 |
+
batch_size: 1
|
53 |
+
|
54 |
+
valid_dataloader_opts:
|
55 |
+
batch_size: 1
|
56 |
+
|
57 |
+
test_dataloader_opts:
|
58 |
+
batch_size: 1
|
59 |
+
|
60 |
+
# Model parameters
|
61 |
+
activation: &id001 !name:torch.nn.LeakyReLU
|
62 |
+
dnn_layers: 2
|
63 |
+
dnn_neurons: 1024
|
64 |
+
freeze_wav2vec: false
|
65 |
+
|
66 |
+
# Outputs
|
67 |
+
output_neurons: 41 # BPE size, index(blank/eos/bos) = 0
|
68 |
+
|
69 |
+
# Decoding parameters
|
70 |
+
blank_index: 0
|
71 |
+
bos_index: 1
|
72 |
+
eos_index: 2
|
73 |
+
|
74 |
+
#
|
75 |
+
# Functions and classes
|
76 |
+
#
|
77 |
+
epoch_counter: &id008 !new:speechbrain.utils.epoch_loop.EpochCounter
|
78 |
+
|
79 |
+
limit: 12
|
80 |
+
|
81 |
+
augmentation: !new:speechbrain.lobes.augment.TimeDomainSpecAugment
|
82 |
+
sample_rate: 16000
|
83 |
+
speeds: [95, 100, 105]
|
84 |
+
|
85 |
+
enc: &id003 !new:speechbrain.lobes.models.VanillaNN.VanillaNN
|
86 |
+
input_shape: [null, null, 1024]
|
87 |
+
activation: *id001
|
88 |
+
dnn_blocks: 2
|
89 |
+
dnn_neurons: 1024
|
90 |
+
|
91 |
+
wav2vec2: &id002 !new:speechbrain.lobes.models.huggingface_wav2vec.HuggingFaceWav2Vec2
|
92 |
+
source: wavlm-large/
|
93 |
+
output_norm: true
|
94 |
+
freeze: false
|
95 |
+
freeze_feature_extractor: true
|
96 |
+
save_path: partly_frozen_splitted_wavlm/save/wav2vec2_hubert_checkpoint
|
97 |
+
|
98 |
+
#####
|
99 |
+
# Uncomment this block if you prefer to use a Fairseq pretrained model instead
|
100 |
+
# of a HuggingFace one. Here, we provide an URL that is obtained from the
|
101 |
+
# Fairseq github for the multilingual XLSR.
|
102 |
+
#
|
103 |
+
#wav2vec2_url: https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_960h_pl.pt
|
104 |
+
#wav2vec2: !new:speechbrain.lobes.models.fairseq_wav2vec.FairseqWav2Vec2
|
105 |
+
# pretrained_path: !ref <wav2vec2_url>
|
106 |
+
# output_norm: True
|
107 |
+
# freeze: False
|
108 |
+
# save_path: !ref <save_folder>/wav2vec2_checkpoint/model.pt
|
109 |
+
|
110 |
+
ctc_lin: &id004 !new:speechbrain.nnet.linear.Linear
|
111 |
+
|
112 |
+
input_size: 1024
|
113 |
+
n_neurons: 41
|
114 |
+
|
115 |
+
log_softmax: !new:speechbrain.nnet.activations.Softmax
|
116 |
+
apply_log: true
|
117 |
+
|
118 |
+
ctc_cost: !name:speechbrain.nnet.losses.ctc_loss
|
119 |
+
blank_index: 0
|
120 |
+
|
121 |
+
modules:
|
122 |
+
wav2vec2: *id002
|
123 |
+
enc: *id003
|
124 |
+
ctc_lin: *id004
|
125 |
+
model: &id005 !new:torch.nn.ModuleList
|
126 |
+
- [*id003, *id004]
|
127 |
+
model_opt_class: !name:torch.optim.Adadelta
|
128 |
+
lr: 1
|
129 |
+
rho: 0.95
|
130 |
+
eps: 1.e-8
|
131 |
+
|
132 |
+
wav2vec_opt_class: !name:torch.optim.Adam
|
133 |
+
lr: 0.0001
|
134 |
+
|
135 |
+
lr_annealing_model: &id006 !new:speechbrain.nnet.schedulers.NewBobScheduler
|
136 |
+
initial_value: 1
|
137 |
+
improvement_threshold: 0.0025
|
138 |
+
annealing_factor: 0.8
|
139 |
+
patient: 0
|
140 |
+
|
141 |
+
lr_annealing_wav2vec: &id007 !new:speechbrain.nnet.schedulers.NewBobScheduler
|
142 |
+
initial_value: 0.0001
|
143 |
+
improvement_threshold: 0.0025
|
144 |
+
annealing_factor: 0.9
|
145 |
+
patient: 0
|
146 |
+
|
147 |
+
|
148 |
+
checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
|
149 |
+
checkpoints_dir: partly_frozen_splitted_wavlm/save
|
150 |
+
recoverables:
|
151 |
+
wav2vec2: *id002
|
152 |
+
model: *id005
|
153 |
+
scheduler_model: *id006
|
154 |
+
scheduler_wav2vec: *id007
|
155 |
+
counter: *id008
|
156 |
+
train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
|
157 |
+
save_file: partly_frozen_splitted_wavlm/train_log.txt
|
158 |
+
|
159 |
+
error_rate_computer: !name:speechbrain.utils.metric_stats.ErrorRateStats
|
160 |
+
|
161 |
+
cer_computer: !name:speechbrain.utils.metric_stats.ErrorRateStats
|
162 |
+
split_tokens: true
|
partly_frozen_splitted_wavlm/log.txt
ADDED
@@ -0,0 +1,1998 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
2023-01-07 15:57:01,210 - speechbrain.core - INFO - Beginning experiment!
|
2 |
+
2023-01-07 15:57:01,210 - speechbrain.core - INFO - Experiment folder: partly_frozen_splitted_wavlm
|
3 |
+
2023-01-07 15:57:01,912 - speechbrain.utils.superpowers - DEBUG - abkhazia==1.0
|
4 |
+
absl-py==0.11.0
|
5 |
+
aiohttp==3.8.0
|
6 |
+
aiosignal==1.2.0
|
7 |
+
alabaster==0.7.12
|
8 |
+
alembic==1.7.4
|
9 |
+
altgraph==0.17
|
10 |
+
antlr4-python3-runtime==4.8
|
11 |
+
appdirs==1.4.4
|
12 |
+
argcomplete==1.12.2
|
13 |
+
argon2-cffi==20.1.0
|
14 |
+
asgiref==3.6.0
|
15 |
+
astunparse==1.6.3
|
16 |
+
async-generator==1.10
|
17 |
+
async-timeout==4.0.0
|
18 |
+
attrdict==2.0.1
|
19 |
+
attrs==20.3.0
|
20 |
+
audeer==1.16.0
|
21 |
+
audformat==0.11.5
|
22 |
+
audinterface==0.7.0
|
23 |
+
audiofile==1.0.0
|
24 |
+
audiomentations==0.25.0
|
25 |
+
audioread==2.1.9
|
26 |
+
audobject==0.4.14
|
27 |
+
audresample==0.1.6
|
28 |
+
-e git+https://github.com/facebookresearch/WavAugment.git@54afcdb00ccc852c2f030f239f8532c9562b550e#egg=augment
|
29 |
+
autopage==0.4.0
|
30 |
+
Babel==2.9.0
|
31 |
+
backcall==0.2.0
|
32 |
+
beautifulsoup4==4.10.0
|
33 |
+
black==19.10b0
|
34 |
+
bleach==3.3.0
|
35 |
+
boto3==1.20.2
|
36 |
+
botocore==1.23.2
|
37 |
+
braceexpand==0.1.7
|
38 |
+
cachetools==4.2.0
|
39 |
+
certifi @ file:///croot/certifi_1671487769961/work/certifi
|
40 |
+
cffi==1.14.3
|
41 |
+
cfgv==3.2.0
|
42 |
+
chardet==3.0.4
|
43 |
+
charset-normalizer==2.0.7
|
44 |
+
click==7.1.2
|
45 |
+
cliff==3.9.0
|
46 |
+
clldutils==3.5.4
|
47 |
+
cmaes==0.8.2
|
48 |
+
cmake==3.18.4.post1
|
49 |
+
cmd2==2.2.0
|
50 |
+
colorama==0.4.4
|
51 |
+
colorlog==4.6.2
|
52 |
+
configparser==5.1.0
|
53 |
+
cryptography==38.0.4
|
54 |
+
csvw==1.8.1
|
55 |
+
cycler==0.10.0
|
56 |
+
Cython==0.29.21
|
57 |
+
dataclasses==0.6
|
58 |
+
datasets==1.5.0
|
59 |
+
decorator==4.4.2
|
60 |
+
deepspeech==0.9.1
|
61 |
+
defusedxml==0.7.1
|
62 |
+
denoiser==0.1.5
|
63 |
+
dill==0.3.3
|
64 |
+
Distance==0.1.3
|
65 |
+
distlib==0.3.1
|
66 |
+
Django==3.2.16
|
67 |
+
django-auditlog==2.2.1
|
68 |
+
django-filter==22.1
|
69 |
+
django-js-asset==1.2.2
|
70 |
+
django-mptt==0.14.0
|
71 |
+
djangorestframework==3.14.0
|
72 |
+
docker-pycreds==0.4.0
|
73 |
+
docopt==0.6.2
|
74 |
+
docutils==0.16
|
75 |
+
drf-excel==2.2.0
|
76 |
+
drf-flex-fields==1.0.0
|
77 |
+
drf-renderer-xlsx==0.4.1
|
78 |
+
easyocr==1.2.1
|
79 |
+
editdistance==0.6.0
|
80 |
+
emoji==2.2.0
|
81 |
+
entrypoints==0.3
|
82 |
+
et-xmlfile==1.1.0
|
83 |
+
exceptiongroup==1.1.0
|
84 |
+
farasapy==0.0.14
|
85 |
+
fasttext==0.9.2
|
86 |
+
ffmpeg-python==0.2.0
|
87 |
+
filelock==3.0.12
|
88 |
+
flake8==3.7.9
|
89 |
+
flatbuffers==1.12
|
90 |
+
frozendict==2.0.7
|
91 |
+
frozenlist==1.2.0
|
92 |
+
fsspec==2021.11.0
|
93 |
+
future==0.18.2
|
94 |
+
g2p-en==2.1.0
|
95 |
+
gast==0.3.3
|
96 |
+
gdown==4.2.0
|
97 |
+
gensim==4.0.1
|
98 |
+
gitdb==4.0.9
|
99 |
+
GitPython==3.1.24
|
100 |
+
google-auth==1.24.0
|
101 |
+
google-auth-oauthlib==0.4.2
|
102 |
+
google-pasta==0.2.0
|
103 |
+
greenlet==1.1.2
|
104 |
+
grpcio==1.32.0
|
105 |
+
h5features==1.3.2
|
106 |
+
h5py==2.10.0
|
107 |
+
htk-io==0.5
|
108 |
+
huggingface-hub==0.9.1
|
109 |
+
hydra-colorlog==0.1.4
|
110 |
+
hydra-core==0.11.3
|
111 |
+
HyperPyYAML==1.1.0
|
112 |
+
hypothesis==6.61.2
|
113 |
+
identify==1.5.10
|
114 |
+
idna==2.10
|
115 |
+
imageio==2.9.0
|
116 |
+
imagesize==1.2.0
|
117 |
+
importlib-metadata==4.8.1
|
118 |
+
importlib-resources==5.2.2
|
119 |
+
inflect==5.3.0
|
120 |
+
ipadic==1.0.0
|
121 |
+
ipykernel==5.3.4
|
122 |
+
ipython==7.19.0
|
123 |
+
ipython-genutils==0.2.0
|
124 |
+
ipywidgets==7.6.3
|
125 |
+
iso-639==0.4.5
|
126 |
+
isodate==0.6.0
|
127 |
+
isort==4.3.21
|
128 |
+
jedi==0.17.2
|
129 |
+
jieba==0.42.1
|
130 |
+
Jinja2==2.11.2
|
131 |
+
jiwer==2.2.0
|
132 |
+
jmespath==0.10.0
|
133 |
+
joblib==0.17.0
|
134 |
+
jsonschema==3.2.0
|
135 |
+
julius==0.2.7
|
136 |
+
jupyter-client==6.1.7
|
137 |
+
jupyter-core==4.7.0
|
138 |
+
jupyterlab-pygments==0.1.2
|
139 |
+
jupyterlab-widgets==1.0.0
|
140 |
+
kaitaistruct==0.9
|
141 |
+
kaldi-io==0.9.4
|
142 |
+
kaldi-python-io==1.2.2
|
143 |
+
kaldiio==2.17.2
|
144 |
+
kenlm @ https://github.com/kpu/kenlm/archive/master.zip
|
145 |
+
Keras-Preprocessing==1.1.2
|
146 |
+
kiwisolver==1.3.1
|
147 |
+
lang-trans==0.6.0
|
148 |
+
latexcodec==2.0.1
|
149 |
+
ldap3==2.9.1
|
150 |
+
librosa==0.9.0
|
151 |
+
llvmlite==0.35.0
|
152 |
+
lxml==4.9.0
|
153 |
+
Mako==1.1.5
|
154 |
+
Markdown==3.3.3
|
155 |
+
MarkupSafe==1.1.1
|
156 |
+
marshmallow==3.14.0
|
157 |
+
matplotlib==3.3.3
|
158 |
+
mccabe==0.6.1
|
159 |
+
mcd==0.4
|
160 |
+
mecab-python3==1.0.3
|
161 |
+
megatron-lm==2.2.0
|
162 |
+
mido==1.2.10
|
163 |
+
mistune==0.8.4
|
164 |
+
more-itertools==8.6.0
|
165 |
+
mpmath==1.2.1
|
166 |
+
multidict==5.2.0
|
167 |
+
multiprocess==0.70.11.1
|
168 |
+
nbclient==0.5.3
|
169 |
+
nbconvert==6.0.7
|
170 |
+
nbformat==5.1.3
|
171 |
+
NEMO==4.3.2
|
172 |
+
nemo-toolkit==1.4.0
|
173 |
+
nest-asyncio==1.5.1
|
174 |
+
networkx==2.5
|
175 |
+
nltk==3.5
|
176 |
+
nodeenv==1.5.0
|
177 |
+
notebook==6.3.0
|
178 |
+
numba==0.52.0
|
179 |
+
numpy==1.19.4
|
180 |
+
nvidia-cublas-cu11==11.10.3.66
|
181 |
+
nvidia-cuda-nvrtc-cu11==11.7.99
|
182 |
+
nvidia-cuda-runtime-cu11==11.7.99
|
183 |
+
nvidia-cudnn-cu11==8.5.0.96
|
184 |
+
oauthlib==3.1.0
|
185 |
+
omegaconf==1.4.1
|
186 |
+
onnx==1.10.2
|
187 |
+
OpenCC==1.1.2
|
188 |
+
opencv-python==4.4.0.46
|
189 |
+
openpyxl==3.0.9
|
190 |
+
opensmile==2.2.0
|
191 |
+
opt-einsum==3.3.0
|
192 |
+
optuna==2.10.0
|
193 |
+
oyaml==1.0
|
194 |
+
packaging==22.0
|
195 |
+
pandas==1.2.5
|
196 |
+
pandocfilters==1.4.3
|
197 |
+
pangu==4.0.6.1
|
198 |
+
parameterized==0.8.1
|
199 |
+
parso==0.7.1
|
200 |
+
pathspec==0.8.1
|
201 |
+
pathtools==0.1.2
|
202 |
+
pbr==5.6.0
|
203 |
+
pefile==2019.4.18
|
204 |
+
pescador==2.1.0
|
205 |
+
pesq==0.0.3
|
206 |
+
pexpect==4.8.0
|
207 |
+
phonemizer==2.2.1
|
208 |
+
pickleshare==0.7.5
|
209 |
+
Pillow==9.3.0
|
210 |
+
pip-api==0.0.23
|
211 |
+
pipreqs==0.4.11
|
212 |
+
pluggy==0.13.1
|
213 |
+
pooch==1.3.0
|
214 |
+
portalocker==2.3.2
|
215 |
+
pre-commit==2.9.0
|
216 |
+
pretty-midi==0.2.9
|
217 |
+
prettytable==2.2.1
|
218 |
+
progressbar2==3.53.1
|
219 |
+
prometheus-client==0.10.1
|
220 |
+
promise==2.3
|
221 |
+
prompt-toolkit==3.0.8
|
222 |
+
protobuf==3.14.0
|
223 |
+
psutil==5.6.6
|
224 |
+
ptyprocess==0.6.0
|
225 |
+
py==1.9.0
|
226 |
+
py-espeak-ng==0.1.8
|
227 |
+
pyannote.audio==1.1.1
|
228 |
+
pyannote.core==4.3
|
229 |
+
pyannote.database==4.1.1
|
230 |
+
pyannote.metrics==3.1
|
231 |
+
pyannote.pipeline==1.5.2
|
232 |
+
PyArabic==0.6.15
|
233 |
+
pyarrow==3.0.0
|
234 |
+
pyasn1==0.4.8
|
235 |
+
pyasn1-modules==0.2.8
|
236 |
+
pybind11==2.8.1
|
237 |
+
pybtex==0.24.0
|
238 |
+
pybtex-docutils==1.0.1
|
239 |
+
pycodestyle==2.5.0
|
240 |
+
pycparser==2.20
|
241 |
+
pyctcdecode==0.4.0
|
242 |
+
pyDeprecate==0.3.1
|
243 |
+
pydub==0.25.1
|
244 |
+
pyflakes==2.1.1
|
245 |
+
Pygments==2.7.2
|
246 |
+
pygtrie==2.5.0
|
247 |
+
pymodbus==2.5.3
|
248 |
+
pyparsing==2.4.7
|
249 |
+
pyperclip==1.8.2
|
250 |
+
pypinyin==0.43.0
|
251 |
+
pyrsistent==0.17.3
|
252 |
+
pyserial==3.5
|
253 |
+
PySocks==1.7.1
|
254 |
+
pystoi==0.3.3
|
255 |
+
pytest==5.4.1
|
256 |
+
pytest-runner==5.3.1
|
257 |
+
python-bidi==0.4.2
|
258 |
+
python-crfsuite==0.9.7
|
259 |
+
python-dateutil==2.8.2
|
260 |
+
python-Levenshtein==0.12.2
|
261 |
+
python-utils==2.4.0
|
262 |
+
pytorch-lightning==1.4.9
|
263 |
+
pytube==11.0.1
|
264 |
+
pytz==2022.6
|
265 |
+
PyWavelets==1.1.1
|
266 |
+
PyYAML==5.3.1
|
267 |
+
pyzmq==20.0.0
|
268 |
+
rapidfuzz==1.8.2
|
269 |
+
regex==2020.11.13
|
270 |
+
requests==2.28.1
|
271 |
+
requests-oauthlib==1.3.0
|
272 |
+
resampy==0.2.2
|
273 |
+
rfc3986==1.4.0
|
274 |
+
rsa==4.7
|
275 |
+
ruamel.yaml==0.17.21
|
276 |
+
ruamel.yaml.clib==0.2.7
|
277 |
+
s3m==1.1.0
|
278 |
+
s3transfer==0.5.0
|
279 |
+
sacrebleu==2.0.0
|
280 |
+
sacremoses==0.0.44
|
281 |
+
scikit-image==0.18.1
|
282 |
+
scikit-learn==0.23.2
|
283 |
+
scipy==1.5.4
|
284 |
+
-e git+https://github.com/sanghack81/SDCIT@00d060dde733fde9345154a494f81e97fb395ca7#egg=SDCIT
|
285 |
+
seaborn==0.11.1
|
286 |
+
segments==2.1.3
|
287 |
+
Send2Trash==1.5.0
|
288 |
+
sentencepiece==0.1.94
|
289 |
+
sentry-sdk==1.4.3
|
290 |
+
shellingham==1.4.0
|
291 |
+
shortuuid==1.0.7
|
292 |
+
SIDEKIT==1.3.8.5.2
|
293 |
+
simplejson==3.17.5
|
294 |
+
six==1.15.0
|
295 |
+
smart-open==5.0.0
|
296 |
+
smmap==5.0.0
|
297 |
+
snowballstemmer==2.0.0
|
298 |
+
sortedcollections==2.1.0
|
299 |
+
sortedcontainers==2.4.0
|
300 |
+
sounddevice==0.4.5
|
301 |
+
SoundFile==0.10.3.post1
|
302 |
+
soupsieve==2.3
|
303 |
+
sox==1.4.1
|
304 |
+
sparsemax==0.1.9
|
305 |
+
speechbrain==0.5.13
|
306 |
+
sphfile==1.0.3
|
307 |
+
Sphinx==3.3.1
|
308 |
+
sphinx-rtd-theme==0.4.3
|
309 |
+
sphinxcontrib-applehelp==1.0.2
|
310 |
+
sphinxcontrib-bibtex==2.4.1
|
311 |
+
sphinxcontrib-devhelp==1.0.2
|
312 |
+
sphinxcontrib-htmlhelp==1.0.3
|
313 |
+
sphinxcontrib-jsmath==1.0.1
|
314 |
+
sphinxcontrib-qthelp==1.0.3
|
315 |
+
sphinxcontrib-serializinghtml==1.1.4
|
316 |
+
SQLAlchemy==1.4.25
|
317 |
+
sqlparse==0.4.2
|
318 |
+
stanza==1.4.2
|
319 |
+
stevedore==3.4.0
|
320 |
+
subprocess32==3.5.4
|
321 |
+
sympy==1.9
|
322 |
+
tabulate==0.8.9
|
323 |
+
tensorboard==2.4.0
|
324 |
+
tensorboard-plugin-wit==1.7.0
|
325 |
+
tensorflow==2.4.0
|
326 |
+
tensorflow-estimator==2.4.0
|
327 |
+
termcolor==1.1.0
|
328 |
+
terminado==0.9.4
|
329 |
+
testpath==0.4.4
|
330 |
+
threadpoolctl==2.1.0
|
331 |
+
tifffile==2020.12.8
|
332 |
+
tikzplotlib==0.9.8
|
333 |
+
tkseem==0.0.3
|
334 |
+
tokenizers==0.10.2
|
335 |
+
toml==0.10.2
|
336 |
+
torch==1.13.1
|
337 |
+
torch-stft==0.1.4
|
338 |
+
torchaudio==0.13.1
|
339 |
+
torchmetrics==0.6.0
|
340 |
+
torchvision==0.14.1
|
341 |
+
tornado==6.1
|
342 |
+
tqdm==4.61.1
|
343 |
+
trackrip==1.2.1
|
344 |
+
traitlets==5.0.5
|
345 |
+
transformers==4.15.0
|
346 |
+
typed-ast==1.4.1
|
347 |
+
typer==0.4.0
|
348 |
+
typing-extensions==3.7.4.3
|
349 |
+
Unidecode==1.3.2
|
350 |
+
uritemplate==3.0.1
|
351 |
+
urllib3==1.26.2
|
352 |
+
virtualenv==20.2.1
|
353 |
+
wandb==0.12.6
|
354 |
+
wcwidth==0.2.5
|
355 |
+
webdataset==0.1.62
|
356 |
+
webencodings==0.5.1
|
357 |
+
Werkzeug==1.0.1
|
358 |
+
wget==3.2
|
359 |
+
widgetsnbextension==3.5.1
|
360 |
+
wordninja==2.0.0
|
361 |
+
wrapt==1.12.1
|
362 |
+
xmltodict==0.13.0
|
363 |
+
xxhash==2.0.0
|
364 |
+
yamllint==1.23.0
|
365 |
+
yarg==0.1.9
|
366 |
+
yarl==1.7.2
|
367 |
+
yaspin==2.1.0
|
368 |
+
youtokentome==1.0.6
|
369 |
+
youtube-dl==2021.6.6
|
370 |
+
zipp==3.6.0
|
371 |
+
|
372 |
+
|
373 |
+
2023-01-07 15:57:02,047 - speechbrain.core - ERROR - Exception:
|
374 |
+
Traceback (most recent call last):
|
375 |
+
File "ctc_train.py", line 305, in <module>
|
376 |
+
train_data, valid_data, test_datasets, label_encoder = dataio_prepare(
|
377 |
+
File "ctc_train.py", line 178, in dataio_prepare
|
378 |
+
train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
|
379 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/speechbrain/dataio/dataset.py", line 365, in from_csv
|
380 |
+
data = load_data_csv(csv_path, replacements)
|
381 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/speechbrain/dataio/dataio.py", line 127, in load_data_csv
|
382 |
+
with open(csv_path, newline="") as csvfile:
|
383 |
+
FileNotFoundError: [Errno 2] No such file or directory: 'test_salah.csv'
|
384 |
+
2023-01-07 15:57:54,234 - speechbrain.core - INFO - Beginning experiment!
|
385 |
+
2023-01-07 15:57:54,234 - speechbrain.core - INFO - Experiment folder: partly_frozen_splitted_wavlm
|
386 |
+
2023-01-07 15:57:54,891 - speechbrain.utils.superpowers - DEBUG - abkhazia==1.0
|
387 |
+
absl-py==0.11.0
|
388 |
+
aiohttp==3.8.0
|
389 |
+
aiosignal==1.2.0
|
390 |
+
alabaster==0.7.12
|
391 |
+
alembic==1.7.4
|
392 |
+
altgraph==0.17
|
393 |
+
antlr4-python3-runtime==4.8
|
394 |
+
appdirs==1.4.4
|
395 |
+
argcomplete==1.12.2
|
396 |
+
argon2-cffi==20.1.0
|
397 |
+
asgiref==3.6.0
|
398 |
+
astunparse==1.6.3
|
399 |
+
async-generator==1.10
|
400 |
+
async-timeout==4.0.0
|
401 |
+
attrdict==2.0.1
|
402 |
+
attrs==20.3.0
|
403 |
+
audeer==1.16.0
|
404 |
+
audformat==0.11.5
|
405 |
+
audinterface==0.7.0
|
406 |
+
audiofile==1.0.0
|
407 |
+
audiomentations==0.25.0
|
408 |
+
audioread==2.1.9
|
409 |
+
audobject==0.4.14
|
410 |
+
audresample==0.1.6
|
411 |
+
-e git+https://github.com/facebookresearch/WavAugment.git@54afcdb00ccc852c2f030f239f8532c9562b550e#egg=augment
|
412 |
+
autopage==0.4.0
|
413 |
+
Babel==2.9.0
|
414 |
+
backcall==0.2.0
|
415 |
+
beautifulsoup4==4.10.0
|
416 |
+
black==19.10b0
|
417 |
+
bleach==3.3.0
|
418 |
+
boto3==1.20.2
|
419 |
+
botocore==1.23.2
|
420 |
+
braceexpand==0.1.7
|
421 |
+
cachetools==4.2.0
|
422 |
+
certifi @ file:///croot/certifi_1671487769961/work/certifi
|
423 |
+
cffi==1.14.3
|
424 |
+
cfgv==3.2.0
|
425 |
+
chardet==3.0.4
|
426 |
+
charset-normalizer==2.0.7
|
427 |
+
click==7.1.2
|
428 |
+
cliff==3.9.0
|
429 |
+
clldutils==3.5.4
|
430 |
+
cmaes==0.8.2
|
431 |
+
cmake==3.18.4.post1
|
432 |
+
cmd2==2.2.0
|
433 |
+
colorama==0.4.4
|
434 |
+
colorlog==4.6.2
|
435 |
+
configparser==5.1.0
|
436 |
+
cryptography==38.0.4
|
437 |
+
csvw==1.8.1
|
438 |
+
cycler==0.10.0
|
439 |
+
Cython==0.29.21
|
440 |
+
dataclasses==0.6
|
441 |
+
datasets==1.5.0
|
442 |
+
decorator==4.4.2
|
443 |
+
deepspeech==0.9.1
|
444 |
+
defusedxml==0.7.1
|
445 |
+
denoiser==0.1.5
|
446 |
+
dill==0.3.3
|
447 |
+
Distance==0.1.3
|
448 |
+
distlib==0.3.1
|
449 |
+
Django==3.2.16
|
450 |
+
django-auditlog==2.2.1
|
451 |
+
django-filter==22.1
|
452 |
+
django-js-asset==1.2.2
|
453 |
+
django-mptt==0.14.0
|
454 |
+
djangorestframework==3.14.0
|
455 |
+
docker-pycreds==0.4.0
|
456 |
+
docopt==0.6.2
|
457 |
+
docutils==0.16
|
458 |
+
drf-excel==2.2.0
|
459 |
+
drf-flex-fields==1.0.0
|
460 |
+
drf-renderer-xlsx==0.4.1
|
461 |
+
easyocr==1.2.1
|
462 |
+
editdistance==0.6.0
|
463 |
+
emoji==2.2.0
|
464 |
+
entrypoints==0.3
|
465 |
+
et-xmlfile==1.1.0
|
466 |
+
exceptiongroup==1.1.0
|
467 |
+
farasapy==0.0.14
|
468 |
+
fasttext==0.9.2
|
469 |
+
ffmpeg-python==0.2.0
|
470 |
+
filelock==3.0.12
|
471 |
+
flake8==3.7.9
|
472 |
+
flatbuffers==1.12
|
473 |
+
frozendict==2.0.7
|
474 |
+
frozenlist==1.2.0
|
475 |
+
fsspec==2021.11.0
|
476 |
+
future==0.18.2
|
477 |
+
g2p-en==2.1.0
|
478 |
+
gast==0.3.3
|
479 |
+
gdown==4.2.0
|
480 |
+
gensim==4.0.1
|
481 |
+
gitdb==4.0.9
|
482 |
+
GitPython==3.1.24
|
483 |
+
google-auth==1.24.0
|
484 |
+
google-auth-oauthlib==0.4.2
|
485 |
+
google-pasta==0.2.0
|
486 |
+
greenlet==1.1.2
|
487 |
+
grpcio==1.32.0
|
488 |
+
h5features==1.3.2
|
489 |
+
h5py==2.10.0
|
490 |
+
htk-io==0.5
|
491 |
+
huggingface-hub==0.9.1
|
492 |
+
hydra-colorlog==0.1.4
|
493 |
+
hydra-core==0.11.3
|
494 |
+
HyperPyYAML==1.1.0
|
495 |
+
hypothesis==6.61.2
|
496 |
+
identify==1.5.10
|
497 |
+
idna==2.10
|
498 |
+
imageio==2.9.0
|
499 |
+
imagesize==1.2.0
|
500 |
+
importlib-metadata==4.8.1
|
501 |
+
importlib-resources==5.2.2
|
502 |
+
inflect==5.3.0
|
503 |
+
ipadic==1.0.0
|
504 |
+
ipykernel==5.3.4
|
505 |
+
ipython==7.19.0
|
506 |
+
ipython-genutils==0.2.0
|
507 |
+
ipywidgets==7.6.3
|
508 |
+
iso-639==0.4.5
|
509 |
+
isodate==0.6.0
|
510 |
+
isort==4.3.21
|
511 |
+
jedi==0.17.2
|
512 |
+
jieba==0.42.1
|
513 |
+
Jinja2==2.11.2
|
514 |
+
jiwer==2.2.0
|
515 |
+
jmespath==0.10.0
|
516 |
+
joblib==0.17.0
|
517 |
+
jsonschema==3.2.0
|
518 |
+
julius==0.2.7
|
519 |
+
jupyter-client==6.1.7
|
520 |
+
jupyter-core==4.7.0
|
521 |
+
jupyterlab-pygments==0.1.2
|
522 |
+
jupyterlab-widgets==1.0.0
|
523 |
+
kaitaistruct==0.9
|
524 |
+
kaldi-io==0.9.4
|
525 |
+
kaldi-python-io==1.2.2
|
526 |
+
kaldiio==2.17.2
|
527 |
+
kenlm @ https://github.com/kpu/kenlm/archive/master.zip
|
528 |
+
Keras-Preprocessing==1.1.2
|
529 |
+
kiwisolver==1.3.1
|
530 |
+
lang-trans==0.6.0
|
531 |
+
latexcodec==2.0.1
|
532 |
+
ldap3==2.9.1
|
533 |
+
librosa==0.9.0
|
534 |
+
llvmlite==0.35.0
|
535 |
+
lxml==4.9.0
|
536 |
+
Mako==1.1.5
|
537 |
+
Markdown==3.3.3
|
538 |
+
MarkupSafe==1.1.1
|
539 |
+
marshmallow==3.14.0
|
540 |
+
matplotlib==3.3.3
|
541 |
+
mccabe==0.6.1
|
542 |
+
mcd==0.4
|
543 |
+
mecab-python3==1.0.3
|
544 |
+
megatron-lm==2.2.0
|
545 |
+
mido==1.2.10
|
546 |
+
mistune==0.8.4
|
547 |
+
more-itertools==8.6.0
|
548 |
+
mpmath==1.2.1
|
549 |
+
multidict==5.2.0
|
550 |
+
multiprocess==0.70.11.1
|
551 |
+
nbclient==0.5.3
|
552 |
+
nbconvert==6.0.7
|
553 |
+
nbformat==5.1.3
|
554 |
+
NEMO==4.3.2
|
555 |
+
nemo-toolkit==1.4.0
|
556 |
+
nest-asyncio==1.5.1
|
557 |
+
networkx==2.5
|
558 |
+
nltk==3.5
|
559 |
+
nodeenv==1.5.0
|
560 |
+
notebook==6.3.0
|
561 |
+
numba==0.52.0
|
562 |
+
numpy==1.19.4
|
563 |
+
nvidia-cublas-cu11==11.10.3.66
|
564 |
+
nvidia-cuda-nvrtc-cu11==11.7.99
|
565 |
+
nvidia-cuda-runtime-cu11==11.7.99
|
566 |
+
nvidia-cudnn-cu11==8.5.0.96
|
567 |
+
oauthlib==3.1.0
|
568 |
+
omegaconf==1.4.1
|
569 |
+
onnx==1.10.2
|
570 |
+
OpenCC==1.1.2
|
571 |
+
opencv-python==4.4.0.46
|
572 |
+
openpyxl==3.0.9
|
573 |
+
opensmile==2.2.0
|
574 |
+
opt-einsum==3.3.0
|
575 |
+
optuna==2.10.0
|
576 |
+
oyaml==1.0
|
577 |
+
packaging==22.0
|
578 |
+
pandas==1.2.5
|
579 |
+
pandocfilters==1.4.3
|
580 |
+
pangu==4.0.6.1
|
581 |
+
parameterized==0.8.1
|
582 |
+
parso==0.7.1
|
583 |
+
pathspec==0.8.1
|
584 |
+
pathtools==0.1.2
|
585 |
+
pbr==5.6.0
|
586 |
+
pefile==2019.4.18
|
587 |
+
pescador==2.1.0
|
588 |
+
pesq==0.0.3
|
589 |
+
pexpect==4.8.0
|
590 |
+
phonemizer==2.2.1
|
591 |
+
pickleshare==0.7.5
|
592 |
+
Pillow==9.3.0
|
593 |
+
pip-api==0.0.23
|
594 |
+
pipreqs==0.4.11
|
595 |
+
pluggy==0.13.1
|
596 |
+
pooch==1.3.0
|
597 |
+
portalocker==2.3.2
|
598 |
+
pre-commit==2.9.0
|
599 |
+
pretty-midi==0.2.9
|
600 |
+
prettytable==2.2.1
|
601 |
+
progressbar2==3.53.1
|
602 |
+
prometheus-client==0.10.1
|
603 |
+
promise==2.3
|
604 |
+
prompt-toolkit==3.0.8
|
605 |
+
protobuf==3.14.0
|
606 |
+
psutil==5.6.6
|
607 |
+
ptyprocess==0.6.0
|
608 |
+
py==1.9.0
|
609 |
+
py-espeak-ng==0.1.8
|
610 |
+
pyannote.audio==1.1.1
|
611 |
+
pyannote.core==4.3
|
612 |
+
pyannote.database==4.1.1
|
613 |
+
pyannote.metrics==3.1
|
614 |
+
pyannote.pipeline==1.5.2
|
615 |
+
PyArabic==0.6.15
|
616 |
+
pyarrow==3.0.0
|
617 |
+
pyasn1==0.4.8
|
618 |
+
pyasn1-modules==0.2.8
|
619 |
+
pybind11==2.8.1
|
620 |
+
pybtex==0.24.0
|
621 |
+
pybtex-docutils==1.0.1
|
622 |
+
pycodestyle==2.5.0
|
623 |
+
pycparser==2.20
|
624 |
+
pyctcdecode==0.4.0
|
625 |
+
pyDeprecate==0.3.1
|
626 |
+
pydub==0.25.1
|
627 |
+
pyflakes==2.1.1
|
628 |
+
Pygments==2.7.2
|
629 |
+
pygtrie==2.5.0
|
630 |
+
pymodbus==2.5.3
|
631 |
+
pyparsing==2.4.7
|
632 |
+
pyperclip==1.8.2
|
633 |
+
pypinyin==0.43.0
|
634 |
+
pyrsistent==0.17.3
|
635 |
+
pyserial==3.5
|
636 |
+
PySocks==1.7.1
|
637 |
+
pystoi==0.3.3
|
638 |
+
pytest==5.4.1
|
639 |
+
pytest-runner==5.3.1
|
640 |
+
python-bidi==0.4.2
|
641 |
+
python-crfsuite==0.9.7
|
642 |
+
python-dateutil==2.8.2
|
643 |
+
python-Levenshtein==0.12.2
|
644 |
+
python-utils==2.4.0
|
645 |
+
pytorch-lightning==1.4.9
|
646 |
+
pytube==11.0.1
|
647 |
+
pytz==2022.6
|
648 |
+
PyWavelets==1.1.1
|
649 |
+
PyYAML==5.3.1
|
650 |
+
pyzmq==20.0.0
|
651 |
+
rapidfuzz==1.8.2
|
652 |
+
regex==2020.11.13
|
653 |
+
requests==2.28.1
|
654 |
+
requests-oauthlib==1.3.0
|
655 |
+
resampy==0.2.2
|
656 |
+
rfc3986==1.4.0
|
657 |
+
rsa==4.7
|
658 |
+
ruamel.yaml==0.17.21
|
659 |
+
ruamel.yaml.clib==0.2.7
|
660 |
+
s3m==1.1.0
|
661 |
+
s3transfer==0.5.0
|
662 |
+
sacrebleu==2.0.0
|
663 |
+
sacremoses==0.0.44
|
664 |
+
scikit-image==0.18.1
|
665 |
+
scikit-learn==0.23.2
|
666 |
+
scipy==1.5.4
|
667 |
+
-e git+https://github.com/sanghack81/SDCIT@00d060dde733fde9345154a494f81e97fb395ca7#egg=SDCIT
|
668 |
+
seaborn==0.11.1
|
669 |
+
segments==2.1.3
|
670 |
+
Send2Trash==1.5.0
|
671 |
+
sentencepiece==0.1.94
|
672 |
+
sentry-sdk==1.4.3
|
673 |
+
shellingham==1.4.0
|
674 |
+
shortuuid==1.0.7
|
675 |
+
SIDEKIT==1.3.8.5.2
|
676 |
+
simplejson==3.17.5
|
677 |
+
six==1.15.0
|
678 |
+
smart-open==5.0.0
|
679 |
+
smmap==5.0.0
|
680 |
+
snowballstemmer==2.0.0
|
681 |
+
sortedcollections==2.1.0
|
682 |
+
sortedcontainers==2.4.0
|
683 |
+
sounddevice==0.4.5
|
684 |
+
SoundFile==0.10.3.post1
|
685 |
+
soupsieve==2.3
|
686 |
+
sox==1.4.1
|
687 |
+
sparsemax==0.1.9
|
688 |
+
speechbrain==0.5.13
|
689 |
+
sphfile==1.0.3
|
690 |
+
Sphinx==3.3.1
|
691 |
+
sphinx-rtd-theme==0.4.3
|
692 |
+
sphinxcontrib-applehelp==1.0.2
|
693 |
+
sphinxcontrib-bibtex==2.4.1
|
694 |
+
sphinxcontrib-devhelp==1.0.2
|
695 |
+
sphinxcontrib-htmlhelp==1.0.3
|
696 |
+
sphinxcontrib-jsmath==1.0.1
|
697 |
+
sphinxcontrib-qthelp==1.0.3
|
698 |
+
sphinxcontrib-serializinghtml==1.1.4
|
699 |
+
SQLAlchemy==1.4.25
|
700 |
+
sqlparse==0.4.2
|
701 |
+
stanza==1.4.2
|
702 |
+
stevedore==3.4.0
|
703 |
+
subprocess32==3.5.4
|
704 |
+
sympy==1.9
|
705 |
+
tabulate==0.8.9
|
706 |
+
tensorboard==2.4.0
|
707 |
+
tensorboard-plugin-wit==1.7.0
|
708 |
+
tensorflow==2.4.0
|
709 |
+
tensorflow-estimator==2.4.0
|
710 |
+
termcolor==1.1.0
|
711 |
+
terminado==0.9.4
|
712 |
+
testpath==0.4.4
|
713 |
+
threadpoolctl==2.1.0
|
714 |
+
tifffile==2020.12.8
|
715 |
+
tikzplotlib==0.9.8
|
716 |
+
tkseem==0.0.3
|
717 |
+
tokenizers==0.10.2
|
718 |
+
toml==0.10.2
|
719 |
+
torch==1.13.1
|
720 |
+
torch-stft==0.1.4
|
721 |
+
torchaudio==0.13.1
|
722 |
+
torchmetrics==0.6.0
|
723 |
+
torchvision==0.14.1
|
724 |
+
tornado==6.1
|
725 |
+
tqdm==4.61.1
|
726 |
+
trackrip==1.2.1
|
727 |
+
traitlets==5.0.5
|
728 |
+
transformers==4.15.0
|
729 |
+
typed-ast==1.4.1
|
730 |
+
typer==0.4.0
|
731 |
+
typing-extensions==3.7.4.3
|
732 |
+
Unidecode==1.3.2
|
733 |
+
uritemplate==3.0.1
|
734 |
+
urllib3==1.26.2
|
735 |
+
virtualenv==20.2.1
|
736 |
+
wandb==0.12.6
|
737 |
+
wcwidth==0.2.5
|
738 |
+
webdataset==0.1.62
|
739 |
+
webencodings==0.5.1
|
740 |
+
Werkzeug==1.0.1
|
741 |
+
wget==3.2
|
742 |
+
widgetsnbextension==3.5.1
|
743 |
+
wordninja==2.0.0
|
744 |
+
wrapt==1.12.1
|
745 |
+
xmltodict==0.13.0
|
746 |
+
xxhash==2.0.0
|
747 |
+
yamllint==1.23.0
|
748 |
+
yarg==0.1.9
|
749 |
+
yarl==1.7.2
|
750 |
+
yaspin==2.1.0
|
751 |
+
youtokentome==1.0.6
|
752 |
+
youtube-dl==2021.6.6
|
753 |
+
zipp==3.6.0
|
754 |
+
|
755 |
+
|
756 |
+
2023-01-07 15:57:54,987 - speechbrain.dataio.encoder - DEBUG - Would load categorical encoding from partly_frozen_splitted_wavlm/save/label_encoder.txt, but file doesn't exist yet.
|
757 |
+
2023-01-07 15:57:54,988 - speechbrain.dataio.encoder - INFO - Moving label 'ت' from index 0, because '<blank>' was put at its place.
|
758 |
+
2023-01-07 15:57:54,988 - speechbrain.dataio.encoder - INFO - Moving label 'ع' from index 1, because '<bos>' was put at its place.
|
759 |
+
2023-01-07 15:57:54,989 - speechbrain.dataio.encoder - INFO - Moving label 'ب' from index 2, because '<eos>' was put at its place.
|
760 |
+
2023-01-07 15:57:54,989 - speechbrain.dataio.encoder - INFO - Load called, but CTCTextEncoder is not empty. Loaded data will overwrite everything. This is normal if there is e.g. an unk label defined at init.
|
761 |
+
2023-01-07 15:57:54,990 - speechbrain.dataio.encoder - DEBUG - Loaded categorical encoding from partly_frozen_splitted_wavlm/save/label_encoder.txt
|
762 |
+
2023-01-07 15:57:54,990 - speechbrain.core - INFO - Info: auto_mix_prec arg from hparam file is used
|
763 |
+
2023-01-07 15:57:54,990 - speechbrain.core - INFO - Info: ckpt_interval_minutes arg from hparam file is used
|
764 |
+
2023-01-07 15:57:57,073 - speechbrain.core - INFO - 313.4M trainable parameters in ASR
|
765 |
+
2023-01-07 15:57:57,075 - speechbrain.utils.checkpoints - INFO - Would load a checkpoint here, but none found yet.
|
766 |
+
2023-01-07 15:57:57,075 - speechbrain.utils.epoch_loop - INFO - Going into epoch 1
|
767 |
+
2023-01-07 15:57:57,132 - speechbrain.core - ERROR - Exception:
|
768 |
+
Traceback (most recent call last):
|
769 |
+
File "ctc_train.py", line 322, in <module>
|
770 |
+
asr_brain.fit(
|
771 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/speechbrain/core.py", line 1153, in fit
|
772 |
+
self._fit_train(train_set=train_set, epoch=epoch, enable=enable)
|
773 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/speechbrain/core.py", line 1009, in _fit_train
|
774 |
+
loss = self.fit_batch(batch)
|
775 |
+
File "ctc_train.py", line 88, in fit_batch
|
776 |
+
predictions = self.compute_forward(batch, sb.Stage.TRAIN)
|
777 |
+
File "ctc_train.py", line 42, in compute_forward
|
778 |
+
feats = self.modules.wav2vec2(wavs)
|
779 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
|
780 |
+
return forward_call(*input, **kwargs)
|
781 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/speechbrain/lobes/models/huggingface_wav2vec.py", line 266, in forward
|
782 |
+
return self.extract_features(wav)
|
783 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/speechbrain/lobes/models/huggingface_wav2vec.py", line 281, in extract_features
|
784 |
+
out = self.model(wav)[0]
|
785 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
|
786 |
+
return forward_call(*input, **kwargs)
|
787 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/transformers/models/wavlm/modeling_wavlm.py", line 1232, in forward
|
788 |
+
extract_features = self.feature_extractor(input_values)
|
789 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
|
790 |
+
return forward_call(*input, **kwargs)
|
791 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/transformers/models/wavlm/modeling_wavlm.py", line 400, in forward
|
792 |
+
hidden_states = conv_layer(hidden_states)
|
793 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
|
794 |
+
return forward_call(*input, **kwargs)
|
795 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/transformers/models/wavlm/modeling_wavlm.py", line 270, in forward
|
796 |
+
hidden_states = self.conv(hidden_states)
|
797 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
|
798 |
+
return forward_call(*input, **kwargs)
|
799 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 313, in forward
|
800 |
+
return self._conv_forward(input, self.weight, self.bias)
|
801 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 309, in _conv_forward
|
802 |
+
return F.conv1d(input, weight, bias, self.stride,
|
803 |
+
RuntimeError: Expected 2D (unbatched) or 3D (batched) input to conv1d, but got input of size: [2, 1, 168960, 1]
|
804 |
+
2023-01-07 15:59:22,733 - speechbrain.core - INFO - Beginning experiment!
|
805 |
+
2023-01-07 15:59:22,733 - speechbrain.core - INFO - Experiment folder: partly_frozen_splitted_wavlm
|
806 |
+
2023-01-07 15:59:23,373 - speechbrain.utils.superpowers - DEBUG - abkhazia==1.0
|
807 |
+
absl-py==0.11.0
|
808 |
+
aiohttp==3.8.0
|
809 |
+
aiosignal==1.2.0
|
810 |
+
alabaster==0.7.12
|
811 |
+
alembic==1.7.4
|
812 |
+
altgraph==0.17
|
813 |
+
antlr4-python3-runtime==4.8
|
814 |
+
appdirs==1.4.4
|
815 |
+
argcomplete==1.12.2
|
816 |
+
argon2-cffi==20.1.0
|
817 |
+
asgiref==3.6.0
|
818 |
+
astunparse==1.6.3
|
819 |
+
async-generator==1.10
|
820 |
+
async-timeout==4.0.0
|
821 |
+
attrdict==2.0.1
|
822 |
+
attrs==20.3.0
|
823 |
+
audeer==1.16.0
|
824 |
+
audformat==0.11.5
|
825 |
+
audinterface==0.7.0
|
826 |
+
audiofile==1.0.0
|
827 |
+
audiomentations==0.25.0
|
828 |
+
audioread==2.1.9
|
829 |
+
audobject==0.4.14
|
830 |
+
audresample==0.1.6
|
831 |
+
-e git+https://github.com/facebookresearch/WavAugment.git@54afcdb00ccc852c2f030f239f8532c9562b550e#egg=augment
|
832 |
+
autopage==0.4.0
|
833 |
+
Babel==2.9.0
|
834 |
+
backcall==0.2.0
|
835 |
+
beautifulsoup4==4.10.0
|
836 |
+
black==19.10b0
|
837 |
+
bleach==3.3.0
|
838 |
+
boto3==1.20.2
|
839 |
+
botocore==1.23.2
|
840 |
+
braceexpand==0.1.7
|
841 |
+
cachetools==4.2.0
|
842 |
+
certifi @ file:///croot/certifi_1671487769961/work/certifi
|
843 |
+
cffi==1.14.3
|
844 |
+
cfgv==3.2.0
|
845 |
+
chardet==3.0.4
|
846 |
+
charset-normalizer==2.0.7
|
847 |
+
click==7.1.2
|
848 |
+
cliff==3.9.0
|
849 |
+
clldutils==3.5.4
|
850 |
+
cmaes==0.8.2
|
851 |
+
cmake==3.18.4.post1
|
852 |
+
cmd2==2.2.0
|
853 |
+
colorama==0.4.4
|
854 |
+
colorlog==4.6.2
|
855 |
+
configparser==5.1.0
|
856 |
+
cryptography==38.0.4
|
857 |
+
csvw==1.8.1
|
858 |
+
cycler==0.10.0
|
859 |
+
Cython==0.29.21
|
860 |
+
dataclasses==0.6
|
861 |
+
datasets==1.5.0
|
862 |
+
decorator==4.4.2
|
863 |
+
deepspeech==0.9.1
|
864 |
+
defusedxml==0.7.1
|
865 |
+
denoiser==0.1.5
|
866 |
+
dill==0.3.3
|
867 |
+
Distance==0.1.3
|
868 |
+
distlib==0.3.1
|
869 |
+
Django==3.2.16
|
870 |
+
django-auditlog==2.2.1
|
871 |
+
django-filter==22.1
|
872 |
+
django-js-asset==1.2.2
|
873 |
+
django-mptt==0.14.0
|
874 |
+
djangorestframework==3.14.0
|
875 |
+
docker-pycreds==0.4.0
|
876 |
+
docopt==0.6.2
|
877 |
+
docutils==0.16
|
878 |
+
drf-excel==2.2.0
|
879 |
+
drf-flex-fields==1.0.0
|
880 |
+
drf-renderer-xlsx==0.4.1
|
881 |
+
easyocr==1.2.1
|
882 |
+
editdistance==0.6.0
|
883 |
+
emoji==2.2.0
|
884 |
+
entrypoints==0.3
|
885 |
+
et-xmlfile==1.1.0
|
886 |
+
exceptiongroup==1.1.0
|
887 |
+
farasapy==0.0.14
|
888 |
+
fasttext==0.9.2
|
889 |
+
ffmpeg-python==0.2.0
|
890 |
+
filelock==3.0.12
|
891 |
+
flake8==3.7.9
|
892 |
+
flatbuffers==1.12
|
893 |
+
frozendict==2.0.7
|
894 |
+
frozenlist==1.2.0
|
895 |
+
fsspec==2021.11.0
|
896 |
+
future==0.18.2
|
897 |
+
g2p-en==2.1.0
|
898 |
+
gast==0.3.3
|
899 |
+
gdown==4.2.0
|
900 |
+
gensim==4.0.1
|
901 |
+
gitdb==4.0.9
|
902 |
+
GitPython==3.1.24
|
903 |
+
google-auth==1.24.0
|
904 |
+
google-auth-oauthlib==0.4.2
|
905 |
+
google-pasta==0.2.0
|
906 |
+
greenlet==1.1.2
|
907 |
+
grpcio==1.32.0
|
908 |
+
h5features==1.3.2
|
909 |
+
h5py==2.10.0
|
910 |
+
htk-io==0.5
|
911 |
+
huggingface-hub==0.9.1
|
912 |
+
hydra-colorlog==0.1.4
|
913 |
+
hydra-core==0.11.3
|
914 |
+
HyperPyYAML==1.1.0
|
915 |
+
hypothesis==6.61.2
|
916 |
+
identify==1.5.10
|
917 |
+
idna==2.10
|
918 |
+
imageio==2.9.0
|
919 |
+
imagesize==1.2.0
|
920 |
+
importlib-metadata==4.8.1
|
921 |
+
importlib-resources==5.2.2
|
922 |
+
inflect==5.3.0
|
923 |
+
ipadic==1.0.0
|
924 |
+
ipykernel==5.3.4
|
925 |
+
ipython==7.19.0
|
926 |
+
ipython-genutils==0.2.0
|
927 |
+
ipywidgets==7.6.3
|
928 |
+
iso-639==0.4.5
|
929 |
+
isodate==0.6.0
|
930 |
+
isort==4.3.21
|
931 |
+
jedi==0.17.2
|
932 |
+
jieba==0.42.1
|
933 |
+
Jinja2==2.11.2
|
934 |
+
jiwer==2.2.0
|
935 |
+
jmespath==0.10.0
|
936 |
+
joblib==0.17.0
|
937 |
+
jsonschema==3.2.0
|
938 |
+
julius==0.2.7
|
939 |
+
jupyter-client==6.1.7
|
940 |
+
jupyter-core==4.7.0
|
941 |
+
jupyterlab-pygments==0.1.2
|
942 |
+
jupyterlab-widgets==1.0.0
|
943 |
+
kaitaistruct==0.9
|
944 |
+
kaldi-io==0.9.4
|
945 |
+
kaldi-python-io==1.2.2
|
946 |
+
kaldiio==2.17.2
|
947 |
+
kenlm @ https://github.com/kpu/kenlm/archive/master.zip
|
948 |
+
Keras-Preprocessing==1.1.2
|
949 |
+
kiwisolver==1.3.1
|
950 |
+
lang-trans==0.6.0
|
951 |
+
latexcodec==2.0.1
|
952 |
+
ldap3==2.9.1
|
953 |
+
librosa==0.9.0
|
954 |
+
llvmlite==0.35.0
|
955 |
+
lxml==4.9.0
|
956 |
+
Mako==1.1.5
|
957 |
+
Markdown==3.3.3
|
958 |
+
MarkupSafe==1.1.1
|
959 |
+
marshmallow==3.14.0
|
960 |
+
matplotlib==3.3.3
|
961 |
+
mccabe==0.6.1
|
962 |
+
mcd==0.4
|
963 |
+
mecab-python3==1.0.3
|
964 |
+
megatron-lm==2.2.0
|
965 |
+
mido==1.2.10
|
966 |
+
mistune==0.8.4
|
967 |
+
more-itertools==8.6.0
|
968 |
+
mpmath==1.2.1
|
969 |
+
multidict==5.2.0
|
970 |
+
multiprocess==0.70.11.1
|
971 |
+
nbclient==0.5.3
|
972 |
+
nbconvert==6.0.7
|
973 |
+
nbformat==5.1.3
|
974 |
+
NEMO==4.3.2
|
975 |
+
nemo-toolkit==1.4.0
|
976 |
+
nest-asyncio==1.5.1
|
977 |
+
networkx==2.5
|
978 |
+
nltk==3.5
|
979 |
+
nodeenv==1.5.0
|
980 |
+
notebook==6.3.0
|
981 |
+
numba==0.52.0
|
982 |
+
numpy==1.19.4
|
983 |
+
nvidia-cublas-cu11==11.10.3.66
|
984 |
+
nvidia-cuda-nvrtc-cu11==11.7.99
|
985 |
+
nvidia-cuda-runtime-cu11==11.7.99
|
986 |
+
nvidia-cudnn-cu11==8.5.0.96
|
987 |
+
oauthlib==3.1.0
|
988 |
+
omegaconf==1.4.1
|
989 |
+
onnx==1.10.2
|
990 |
+
OpenCC==1.1.2
|
991 |
+
opencv-python==4.4.0.46
|
992 |
+
openpyxl==3.0.9
|
993 |
+
opensmile==2.2.0
|
994 |
+
opt-einsum==3.3.0
|
995 |
+
optuna==2.10.0
|
996 |
+
oyaml==1.0
|
997 |
+
packaging==22.0
|
998 |
+
pandas==1.2.5
|
999 |
+
pandocfilters==1.4.3
|
1000 |
+
pangu==4.0.6.1
|
1001 |
+
parameterized==0.8.1
|
1002 |
+
parso==0.7.1
|
1003 |
+
pathspec==0.8.1
|
1004 |
+
pathtools==0.1.2
|
1005 |
+
pbr==5.6.0
|
1006 |
+
pefile==2019.4.18
|
1007 |
+
pescador==2.1.0
|
1008 |
+
pesq==0.0.3
|
1009 |
+
pexpect==4.8.0
|
1010 |
+
phonemizer==2.2.1
|
1011 |
+
pickleshare==0.7.5
|
1012 |
+
Pillow==9.3.0
|
1013 |
+
pip-api==0.0.23
|
1014 |
+
pipreqs==0.4.11
|
1015 |
+
pluggy==0.13.1
|
1016 |
+
pooch==1.3.0
|
1017 |
+
portalocker==2.3.2
|
1018 |
+
pre-commit==2.9.0
|
1019 |
+
pretty-midi==0.2.9
|
1020 |
+
prettytable==2.2.1
|
1021 |
+
progressbar2==3.53.1
|
1022 |
+
prometheus-client==0.10.1
|
1023 |
+
promise==2.3
|
1024 |
+
prompt-toolkit==3.0.8
|
1025 |
+
protobuf==3.14.0
|
1026 |
+
psutil==5.6.6
|
1027 |
+
ptyprocess==0.6.0
|
1028 |
+
py==1.9.0
|
1029 |
+
py-espeak-ng==0.1.8
|
1030 |
+
pyannote.audio==1.1.1
|
1031 |
+
pyannote.core==4.3
|
1032 |
+
pyannote.database==4.1.1
|
1033 |
+
pyannote.metrics==3.1
|
1034 |
+
pyannote.pipeline==1.5.2
|
1035 |
+
PyArabic==0.6.15
|
1036 |
+
pyarrow==3.0.0
|
1037 |
+
pyasn1==0.4.8
|
1038 |
+
pyasn1-modules==0.2.8
|
1039 |
+
pybind11==2.8.1
|
1040 |
+
pybtex==0.24.0
|
1041 |
+
pybtex-docutils==1.0.1
|
1042 |
+
pycodestyle==2.5.0
|
1043 |
+
pycparser==2.20
|
1044 |
+
pyctcdecode==0.4.0
|
1045 |
+
pyDeprecate==0.3.1
|
1046 |
+
pydub==0.25.1
|
1047 |
+
pyflakes==2.1.1
|
1048 |
+
Pygments==2.7.2
|
1049 |
+
pygtrie==2.5.0
|
1050 |
+
pymodbus==2.5.3
|
1051 |
+
pyparsing==2.4.7
|
1052 |
+
pyperclip==1.8.2
|
1053 |
+
pypinyin==0.43.0
|
1054 |
+
pyrsistent==0.17.3
|
1055 |
+
pyserial==3.5
|
1056 |
+
PySocks==1.7.1
|
1057 |
+
pystoi==0.3.3
|
1058 |
+
pytest==5.4.1
|
1059 |
+
pytest-runner==5.3.1
|
1060 |
+
python-bidi==0.4.2
|
1061 |
+
python-crfsuite==0.9.7
|
1062 |
+
python-dateutil==2.8.2
|
1063 |
+
python-Levenshtein==0.12.2
|
1064 |
+
python-utils==2.4.0
|
1065 |
+
pytorch-lightning==1.4.9
|
1066 |
+
pytube==11.0.1
|
1067 |
+
pytz==2022.6
|
1068 |
+
PyWavelets==1.1.1
|
1069 |
+
PyYAML==5.3.1
|
1070 |
+
pyzmq==20.0.0
|
1071 |
+
rapidfuzz==1.8.2
|
1072 |
+
regex==2020.11.13
|
1073 |
+
requests==2.28.1
|
1074 |
+
requests-oauthlib==1.3.0
|
1075 |
+
resampy==0.2.2
|
1076 |
+
rfc3986==1.4.0
|
1077 |
+
rsa==4.7
|
1078 |
+
ruamel.yaml==0.17.21
|
1079 |
+
ruamel.yaml.clib==0.2.7
|
1080 |
+
s3m==1.1.0
|
1081 |
+
s3transfer==0.5.0
|
1082 |
+
sacrebleu==2.0.0
|
1083 |
+
sacremoses==0.0.44
|
1084 |
+
scikit-image==0.18.1
|
1085 |
+
scikit-learn==0.23.2
|
1086 |
+
scipy==1.5.4
|
1087 |
+
-e git+https://github.com/sanghack81/SDCIT@00d060dde733fde9345154a494f81e97fb395ca7#egg=SDCIT
|
1088 |
+
seaborn==0.11.1
|
1089 |
+
segments==2.1.3
|
1090 |
+
Send2Trash==1.5.0
|
1091 |
+
sentencepiece==0.1.94
|
1092 |
+
sentry-sdk==1.4.3
|
1093 |
+
shellingham==1.4.0
|
1094 |
+
shortuuid==1.0.7
|
1095 |
+
SIDEKIT==1.3.8.5.2
|
1096 |
+
simplejson==3.17.5
|
1097 |
+
six==1.15.0
|
1098 |
+
smart-open==5.0.0
|
1099 |
+
smmap==5.0.0
|
1100 |
+
snowballstemmer==2.0.0
|
1101 |
+
sortedcollections==2.1.0
|
1102 |
+
sortedcontainers==2.4.0
|
1103 |
+
sounddevice==0.4.5
|
1104 |
+
SoundFile==0.10.3.post1
|
1105 |
+
soupsieve==2.3
|
1106 |
+
sox==1.4.1
|
1107 |
+
sparsemax==0.1.9
|
1108 |
+
speechbrain==0.5.13
|
1109 |
+
sphfile==1.0.3
|
1110 |
+
Sphinx==3.3.1
|
1111 |
+
sphinx-rtd-theme==0.4.3
|
1112 |
+
sphinxcontrib-applehelp==1.0.2
|
1113 |
+
sphinxcontrib-bibtex==2.4.1
|
1114 |
+
sphinxcontrib-devhelp==1.0.2
|
1115 |
+
sphinxcontrib-htmlhelp==1.0.3
|
1116 |
+
sphinxcontrib-jsmath==1.0.1
|
1117 |
+
sphinxcontrib-qthelp==1.0.3
|
1118 |
+
sphinxcontrib-serializinghtml==1.1.4
|
1119 |
+
SQLAlchemy==1.4.25
|
1120 |
+
sqlparse==0.4.2
|
1121 |
+
stanza==1.4.2
|
1122 |
+
stevedore==3.4.0
|
1123 |
+
subprocess32==3.5.4
|
1124 |
+
sympy==1.9
|
1125 |
+
tabulate==0.8.9
|
1126 |
+
tensorboard==2.4.0
|
1127 |
+
tensorboard-plugin-wit==1.7.0
|
1128 |
+
tensorflow==2.4.0
|
1129 |
+
tensorflow-estimator==2.4.0
|
1130 |
+
termcolor==1.1.0
|
1131 |
+
terminado==0.9.4
|
1132 |
+
testpath==0.4.4
|
1133 |
+
threadpoolctl==2.1.0
|
1134 |
+
tifffile==2020.12.8
|
1135 |
+
tikzplotlib==0.9.8
|
1136 |
+
tkseem==0.0.3
|
1137 |
+
tokenizers==0.10.2
|
1138 |
+
toml==0.10.2
|
1139 |
+
torch==1.13.1
|
1140 |
+
torch-stft==0.1.4
|
1141 |
+
torchaudio==0.13.1
|
1142 |
+
torchmetrics==0.6.0
|
1143 |
+
torchvision==0.14.1
|
1144 |
+
tornado==6.1
|
1145 |
+
tqdm==4.61.1
|
1146 |
+
trackrip==1.2.1
|
1147 |
+
traitlets==5.0.5
|
1148 |
+
transformers==4.15.0
|
1149 |
+
typed-ast==1.4.1
|
1150 |
+
typer==0.4.0
|
1151 |
+
typing-extensions==3.7.4.3
|
1152 |
+
Unidecode==1.3.2
|
1153 |
+
uritemplate==3.0.1
|
1154 |
+
urllib3==1.26.2
|
1155 |
+
virtualenv==20.2.1
|
1156 |
+
wandb==0.12.6
|
1157 |
+
wcwidth==0.2.5
|
1158 |
+
webdataset==0.1.62
|
1159 |
+
webencodings==0.5.1
|
1160 |
+
Werkzeug==1.0.1
|
1161 |
+
wget==3.2
|
1162 |
+
widgetsnbextension==3.5.1
|
1163 |
+
wordninja==2.0.0
|
1164 |
+
wrapt==1.12.1
|
1165 |
+
xmltodict==0.13.0
|
1166 |
+
xxhash==2.0.0
|
1167 |
+
yamllint==1.23.0
|
1168 |
+
yarg==0.1.9
|
1169 |
+
yarl==1.7.2
|
1170 |
+
yaspin==2.1.0
|
1171 |
+
youtokentome==1.0.6
|
1172 |
+
youtube-dl==2021.6.6
|
1173 |
+
zipp==3.6.0
|
1174 |
+
|
1175 |
+
|
1176 |
+
2023-01-07 15:59:23,493 - speechbrain.dataio.encoder - DEBUG - Loaded categorical encoding from partly_frozen_splitted_wavlm/save/label_encoder.txt
|
1177 |
+
2023-01-07 15:59:23,493 - speechbrain.dataio.encoder - INFO - Load called, but CTCTextEncoder is not empty. Loaded data will overwrite everything. This is normal if there is e.g. an unk label defined at init.
|
1178 |
+
2023-01-07 15:59:23,494 - speechbrain.dataio.encoder - DEBUG - Loaded categorical encoding from partly_frozen_splitted_wavlm/save/label_encoder.txt
|
1179 |
+
2023-01-07 15:59:23,495 - speechbrain.core - INFO - Info: auto_mix_prec arg from hparam file is used
|
1180 |
+
2023-01-07 15:59:23,495 - speechbrain.core - INFO - Info: ckpt_interval_minutes arg from hparam file is used
|
1181 |
+
2023-01-07 15:59:24,946 - speechbrain.core - INFO - 313.4M trainable parameters in ASR
|
1182 |
+
2023-01-07 15:59:24,949 - speechbrain.utils.checkpoints - INFO - Would load a checkpoint here, but none found yet.
|
1183 |
+
2023-01-07 15:59:24,949 - speechbrain.utils.epoch_loop - INFO - Going into epoch 1
|
1184 |
+
2023-01-07 15:59:27,528 - speechbrain.core - ERROR - Exception:
|
1185 |
+
Traceback (most recent call last):
|
1186 |
+
File "ctc_train.py", line 322, in <module>
|
1187 |
+
asr_brain.fit(
|
1188 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/speechbrain/core.py", line 1153, in fit
|
1189 |
+
self._fit_train(train_set=train_set, epoch=epoch, enable=enable)
|
1190 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/speechbrain/core.py", line 1009, in _fit_train
|
1191 |
+
loss = self.fit_batch(batch)
|
1192 |
+
File "ctc_train.py", line 92, in fit_batch
|
1193 |
+
self.wav2vec_optimizer.step()
|
1194 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/torch/optim/optimizer.py", line 140, in wrapper
|
1195 |
+
out = func(*args, **kwargs)
|
1196 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/torch/optim/optimizer.py", line 23, in _use_grad
|
1197 |
+
ret = func(self, *args, **kwargs)
|
1198 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/torch/optim/adam.py", line 220, in step
|
1199 |
+
state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
|
1200 |
+
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 16.00 MiB (GPU 0; 5.80 GiB total capacity; 3.82 GiB already allocated; 52.50 MiB free; 3.94 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
|
1201 |
+
2023-01-07 15:59:54,237 - speechbrain.core - INFO - Beginning experiment!
|
1202 |
+
2023-01-07 15:59:54,237 - speechbrain.core - INFO - Experiment folder: partly_frozen_splitted_wavlm
|
1203 |
+
2023-01-07 15:59:54,868 - speechbrain.utils.superpowers - DEBUG - abkhazia==1.0
|
1204 |
+
absl-py==0.11.0
|
1205 |
+
aiohttp==3.8.0
|
1206 |
+
aiosignal==1.2.0
|
1207 |
+
alabaster==0.7.12
|
1208 |
+
alembic==1.7.4
|
1209 |
+
altgraph==0.17
|
1210 |
+
antlr4-python3-runtime==4.8
|
1211 |
+
appdirs==1.4.4
|
1212 |
+
argcomplete==1.12.2
|
1213 |
+
argon2-cffi==20.1.0
|
1214 |
+
asgiref==3.6.0
|
1215 |
+
astunparse==1.6.3
|
1216 |
+
async-generator==1.10
|
1217 |
+
async-timeout==4.0.0
|
1218 |
+
attrdict==2.0.1
|
1219 |
+
attrs==20.3.0
|
1220 |
+
audeer==1.16.0
|
1221 |
+
audformat==0.11.5
|
1222 |
+
audinterface==0.7.0
|
1223 |
+
audiofile==1.0.0
|
1224 |
+
audiomentations==0.25.0
|
1225 |
+
audioread==2.1.9
|
1226 |
+
audobject==0.4.14
|
1227 |
+
audresample==0.1.6
|
1228 |
+
-e git+https://github.com/facebookresearch/WavAugment.git@54afcdb00ccc852c2f030f239f8532c9562b550e#egg=augment
|
1229 |
+
autopage==0.4.0
|
1230 |
+
Babel==2.9.0
|
1231 |
+
backcall==0.2.0
|
1232 |
+
beautifulsoup4==4.10.0
|
1233 |
+
black==19.10b0
|
1234 |
+
bleach==3.3.0
|
1235 |
+
boto3==1.20.2
|
1236 |
+
botocore==1.23.2
|
1237 |
+
braceexpand==0.1.7
|
1238 |
+
cachetools==4.2.0
|
1239 |
+
certifi @ file:///croot/certifi_1671487769961/work/certifi
|
1240 |
+
cffi==1.14.3
|
1241 |
+
cfgv==3.2.0
|
1242 |
+
chardet==3.0.4
|
1243 |
+
charset-normalizer==2.0.7
|
1244 |
+
click==7.1.2
|
1245 |
+
cliff==3.9.0
|
1246 |
+
clldutils==3.5.4
|
1247 |
+
cmaes==0.8.2
|
1248 |
+
cmake==3.18.4.post1
|
1249 |
+
cmd2==2.2.0
|
1250 |
+
colorama==0.4.4
|
1251 |
+
colorlog==4.6.2
|
1252 |
+
configparser==5.1.0
|
1253 |
+
cryptography==38.0.4
|
1254 |
+
csvw==1.8.1
|
1255 |
+
cycler==0.10.0
|
1256 |
+
Cython==0.29.21
|
1257 |
+
dataclasses==0.6
|
1258 |
+
datasets==1.5.0
|
1259 |
+
decorator==4.4.2
|
1260 |
+
deepspeech==0.9.1
|
1261 |
+
defusedxml==0.7.1
|
1262 |
+
denoiser==0.1.5
|
1263 |
+
dill==0.3.3
|
1264 |
+
Distance==0.1.3
|
1265 |
+
distlib==0.3.1
|
1266 |
+
Django==3.2.16
|
1267 |
+
django-auditlog==2.2.1
|
1268 |
+
django-filter==22.1
|
1269 |
+
django-js-asset==1.2.2
|
1270 |
+
django-mptt==0.14.0
|
1271 |
+
djangorestframework==3.14.0
|
1272 |
+
docker-pycreds==0.4.0
|
1273 |
+
docopt==0.6.2
|
1274 |
+
docutils==0.16
|
1275 |
+
drf-excel==2.2.0
|
1276 |
+
drf-flex-fields==1.0.0
|
1277 |
+
drf-renderer-xlsx==0.4.1
|
1278 |
+
easyocr==1.2.1
|
1279 |
+
editdistance==0.6.0
|
1280 |
+
emoji==2.2.0
|
1281 |
+
entrypoints==0.3
|
1282 |
+
et-xmlfile==1.1.0
|
1283 |
+
exceptiongroup==1.1.0
|
1284 |
+
farasapy==0.0.14
|
1285 |
+
fasttext==0.9.2
|
1286 |
+
ffmpeg-python==0.2.0
|
1287 |
+
filelock==3.0.12
|
1288 |
+
flake8==3.7.9
|
1289 |
+
flatbuffers==1.12
|
1290 |
+
frozendict==2.0.7
|
1291 |
+
frozenlist==1.2.0
|
1292 |
+
fsspec==2021.11.0
|
1293 |
+
future==0.18.2
|
1294 |
+
g2p-en==2.1.0
|
1295 |
+
gast==0.3.3
|
1296 |
+
gdown==4.2.0
|
1297 |
+
gensim==4.0.1
|
1298 |
+
gitdb==4.0.9
|
1299 |
+
GitPython==3.1.24
|
1300 |
+
google-auth==1.24.0
|
1301 |
+
google-auth-oauthlib==0.4.2
|
1302 |
+
google-pasta==0.2.0
|
1303 |
+
greenlet==1.1.2
|
1304 |
+
grpcio==1.32.0
|
1305 |
+
h5features==1.3.2
|
1306 |
+
h5py==2.10.0
|
1307 |
+
htk-io==0.5
|
1308 |
+
huggingface-hub==0.9.1
|
1309 |
+
hydra-colorlog==0.1.4
|
1310 |
+
hydra-core==0.11.3
|
1311 |
+
HyperPyYAML==1.1.0
|
1312 |
+
hypothesis==6.61.2
|
1313 |
+
identify==1.5.10
|
1314 |
+
idna==2.10
|
1315 |
+
imageio==2.9.0
|
1316 |
+
imagesize==1.2.0
|
1317 |
+
importlib-metadata==4.8.1
|
1318 |
+
importlib-resources==5.2.2
|
1319 |
+
inflect==5.3.0
|
1320 |
+
ipadic==1.0.0
|
1321 |
+
ipykernel==5.3.4
|
1322 |
+
ipython==7.19.0
|
1323 |
+
ipython-genutils==0.2.0
|
1324 |
+
ipywidgets==7.6.3
|
1325 |
+
iso-639==0.4.5
|
1326 |
+
isodate==0.6.0
|
1327 |
+
isort==4.3.21
|
1328 |
+
jedi==0.17.2
|
1329 |
+
jieba==0.42.1
|
1330 |
+
Jinja2==2.11.2
|
1331 |
+
jiwer==2.2.0
|
1332 |
+
jmespath==0.10.0
|
1333 |
+
joblib==0.17.0
|
1334 |
+
jsonschema==3.2.0
|
1335 |
+
julius==0.2.7
|
1336 |
+
jupyter-client==6.1.7
|
1337 |
+
jupyter-core==4.7.0
|
1338 |
+
jupyterlab-pygments==0.1.2
|
1339 |
+
jupyterlab-widgets==1.0.0
|
1340 |
+
kaitaistruct==0.9
|
1341 |
+
kaldi-io==0.9.4
|
1342 |
+
kaldi-python-io==1.2.2
|
1343 |
+
kaldiio==2.17.2
|
1344 |
+
kenlm @ https://github.com/kpu/kenlm/archive/master.zip
|
1345 |
+
Keras-Preprocessing==1.1.2
|
1346 |
+
kiwisolver==1.3.1
|
1347 |
+
lang-trans==0.6.0
|
1348 |
+
latexcodec==2.0.1
|
1349 |
+
ldap3==2.9.1
|
1350 |
+
librosa==0.9.0
|
1351 |
+
llvmlite==0.35.0
|
1352 |
+
lxml==4.9.0
|
1353 |
+
Mako==1.1.5
|
1354 |
+
Markdown==3.3.3
|
1355 |
+
MarkupSafe==1.1.1
|
1356 |
+
marshmallow==3.14.0
|
1357 |
+
matplotlib==3.3.3
|
1358 |
+
mccabe==0.6.1
|
1359 |
+
mcd==0.4
|
1360 |
+
mecab-python3==1.0.3
|
1361 |
+
megatron-lm==2.2.0
|
1362 |
+
mido==1.2.10
|
1363 |
+
mistune==0.8.4
|
1364 |
+
more-itertools==8.6.0
|
1365 |
+
mpmath==1.2.1
|
1366 |
+
multidict==5.2.0
|
1367 |
+
multiprocess==0.70.11.1
|
1368 |
+
nbclient==0.5.3
|
1369 |
+
nbconvert==6.0.7
|
1370 |
+
nbformat==5.1.3
|
1371 |
+
NEMO==4.3.2
|
1372 |
+
nemo-toolkit==1.4.0
|
1373 |
+
nest-asyncio==1.5.1
|
1374 |
+
networkx==2.5
|
1375 |
+
nltk==3.5
|
1376 |
+
nodeenv==1.5.0
|
1377 |
+
notebook==6.3.0
|
1378 |
+
numba==0.52.0
|
1379 |
+
numpy==1.19.4
|
1380 |
+
nvidia-cublas-cu11==11.10.3.66
|
1381 |
+
nvidia-cuda-nvrtc-cu11==11.7.99
|
1382 |
+
nvidia-cuda-runtime-cu11==11.7.99
|
1383 |
+
nvidia-cudnn-cu11==8.5.0.96
|
1384 |
+
oauthlib==3.1.0
|
1385 |
+
omegaconf==1.4.1
|
1386 |
+
onnx==1.10.2
|
1387 |
+
OpenCC==1.1.2
|
1388 |
+
opencv-python==4.4.0.46
|
1389 |
+
openpyxl==3.0.9
|
1390 |
+
opensmile==2.2.0
|
1391 |
+
opt-einsum==3.3.0
|
1392 |
+
optuna==2.10.0
|
1393 |
+
oyaml==1.0
|
1394 |
+
packaging==22.0
|
1395 |
+
pandas==1.2.5
|
1396 |
+
pandocfilters==1.4.3
|
1397 |
+
pangu==4.0.6.1
|
1398 |
+
parameterized==0.8.1
|
1399 |
+
parso==0.7.1
|
1400 |
+
pathspec==0.8.1
|
1401 |
+
pathtools==0.1.2
|
1402 |
+
pbr==5.6.0
|
1403 |
+
pefile==2019.4.18
|
1404 |
+
pescador==2.1.0
|
1405 |
+
pesq==0.0.3
|
1406 |
+
pexpect==4.8.0
|
1407 |
+
phonemizer==2.2.1
|
1408 |
+
pickleshare==0.7.5
|
1409 |
+
Pillow==9.3.0
|
1410 |
+
pip-api==0.0.23
|
1411 |
+
pipreqs==0.4.11
|
1412 |
+
pluggy==0.13.1
|
1413 |
+
pooch==1.3.0
|
1414 |
+
portalocker==2.3.2
|
1415 |
+
pre-commit==2.9.0
|
1416 |
+
pretty-midi==0.2.9
|
1417 |
+
prettytable==2.2.1
|
1418 |
+
progressbar2==3.53.1
|
1419 |
+
prometheus-client==0.10.1
|
1420 |
+
promise==2.3
|
1421 |
+
prompt-toolkit==3.0.8
|
1422 |
+
protobuf==3.14.0
|
1423 |
+
psutil==5.6.6
|
1424 |
+
ptyprocess==0.6.0
|
1425 |
+
py==1.9.0
|
1426 |
+
py-espeak-ng==0.1.8
|
1427 |
+
pyannote.audio==1.1.1
|
1428 |
+
pyannote.core==4.3
|
1429 |
+
pyannote.database==4.1.1
|
1430 |
+
pyannote.metrics==3.1
|
1431 |
+
pyannote.pipeline==1.5.2
|
1432 |
+
PyArabic==0.6.15
|
1433 |
+
pyarrow==3.0.0
|
1434 |
+
pyasn1==0.4.8
|
1435 |
+
pyasn1-modules==0.2.8
|
1436 |
+
pybind11==2.8.1
|
1437 |
+
pybtex==0.24.0
|
1438 |
+
pybtex-docutils==1.0.1
|
1439 |
+
pycodestyle==2.5.0
|
1440 |
+
pycparser==2.20
|
1441 |
+
pyctcdecode==0.4.0
|
1442 |
+
pyDeprecate==0.3.1
|
1443 |
+
pydub==0.25.1
|
1444 |
+
pyflakes==2.1.1
|
1445 |
+
Pygments==2.7.2
|
1446 |
+
pygtrie==2.5.0
|
1447 |
+
pymodbus==2.5.3
|
1448 |
+
pyparsing==2.4.7
|
1449 |
+
pyperclip==1.8.2
|
1450 |
+
pypinyin==0.43.0
|
1451 |
+
pyrsistent==0.17.3
|
1452 |
+
pyserial==3.5
|
1453 |
+
PySocks==1.7.1
|
1454 |
+
pystoi==0.3.3
|
1455 |
+
pytest==5.4.1
|
1456 |
+
pytest-runner==5.3.1
|
1457 |
+
python-bidi==0.4.2
|
1458 |
+
python-crfsuite==0.9.7
|
1459 |
+
python-dateutil==2.8.2
|
1460 |
+
python-Levenshtein==0.12.2
|
1461 |
+
python-utils==2.4.0
|
1462 |
+
pytorch-lightning==1.4.9
|
1463 |
+
pytube==11.0.1
|
1464 |
+
pytz==2022.6
|
1465 |
+
PyWavelets==1.1.1
|
1466 |
+
PyYAML==5.3.1
|
1467 |
+
pyzmq==20.0.0
|
1468 |
+
rapidfuzz==1.8.2
|
1469 |
+
regex==2020.11.13
|
1470 |
+
requests==2.28.1
|
1471 |
+
requests-oauthlib==1.3.0
|
1472 |
+
resampy==0.2.2
|
1473 |
+
rfc3986==1.4.0
|
1474 |
+
rsa==4.7
|
1475 |
+
ruamel.yaml==0.17.21
|
1476 |
+
ruamel.yaml.clib==0.2.7
|
1477 |
+
s3m==1.1.0
|
1478 |
+
s3transfer==0.5.0
|
1479 |
+
sacrebleu==2.0.0
|
1480 |
+
sacremoses==0.0.44
|
1481 |
+
scikit-image==0.18.1
|
1482 |
+
scikit-learn==0.23.2
|
1483 |
+
scipy==1.5.4
|
1484 |
+
-e git+https://github.com/sanghack81/SDCIT@00d060dde733fde9345154a494f81e97fb395ca7#egg=SDCIT
|
1485 |
+
seaborn==0.11.1
|
1486 |
+
segments==2.1.3
|
1487 |
+
Send2Trash==1.5.0
|
1488 |
+
sentencepiece==0.1.94
|
1489 |
+
sentry-sdk==1.4.3
|
1490 |
+
shellingham==1.4.0
|
1491 |
+
shortuuid==1.0.7
|
1492 |
+
SIDEKIT==1.3.8.5.2
|
1493 |
+
simplejson==3.17.5
|
1494 |
+
six==1.15.0
|
1495 |
+
smart-open==5.0.0
|
1496 |
+
smmap==5.0.0
|
1497 |
+
snowballstemmer==2.0.0
|
1498 |
+
sortedcollections==2.1.0
|
1499 |
+
sortedcontainers==2.4.0
|
1500 |
+
sounddevice==0.4.5
|
1501 |
+
SoundFile==0.10.3.post1
|
1502 |
+
soupsieve==2.3
|
1503 |
+
sox==1.4.1
|
1504 |
+
sparsemax==0.1.9
|
1505 |
+
speechbrain==0.5.13
|
1506 |
+
sphfile==1.0.3
|
1507 |
+
Sphinx==3.3.1
|
1508 |
+
sphinx-rtd-theme==0.4.3
|
1509 |
+
sphinxcontrib-applehelp==1.0.2
|
1510 |
+
sphinxcontrib-bibtex==2.4.1
|
1511 |
+
sphinxcontrib-devhelp==1.0.2
|
1512 |
+
sphinxcontrib-htmlhelp==1.0.3
|
1513 |
+
sphinxcontrib-jsmath==1.0.1
|
1514 |
+
sphinxcontrib-qthelp==1.0.3
|
1515 |
+
sphinxcontrib-serializinghtml==1.1.4
|
1516 |
+
SQLAlchemy==1.4.25
|
1517 |
+
sqlparse==0.4.2
|
1518 |
+
stanza==1.4.2
|
1519 |
+
stevedore==3.4.0
|
1520 |
+
subprocess32==3.5.4
|
1521 |
+
sympy==1.9
|
1522 |
+
tabulate==0.8.9
|
1523 |
+
tensorboard==2.4.0
|
1524 |
+
tensorboard-plugin-wit==1.7.0
|
1525 |
+
tensorflow==2.4.0
|
1526 |
+
tensorflow-estimator==2.4.0
|
1527 |
+
termcolor==1.1.0
|
1528 |
+
terminado==0.9.4
|
1529 |
+
testpath==0.4.4
|
1530 |
+
threadpoolctl==2.1.0
|
1531 |
+
tifffile==2020.12.8
|
1532 |
+
tikzplotlib==0.9.8
|
1533 |
+
tkseem==0.0.3
|
1534 |
+
tokenizers==0.10.2
|
1535 |
+
toml==0.10.2
|
1536 |
+
torch==1.13.1
|
1537 |
+
torch-stft==0.1.4
|
1538 |
+
torchaudio==0.13.1
|
1539 |
+
torchmetrics==0.6.0
|
1540 |
+
torchvision==0.14.1
|
1541 |
+
tornado==6.1
|
1542 |
+
tqdm==4.61.1
|
1543 |
+
trackrip==1.2.1
|
1544 |
+
traitlets==5.0.5
|
1545 |
+
transformers==4.15.0
|
1546 |
+
typed-ast==1.4.1
|
1547 |
+
typer==0.4.0
|
1548 |
+
typing-extensions==3.7.4.3
|
1549 |
+
Unidecode==1.3.2
|
1550 |
+
uritemplate==3.0.1
|
1551 |
+
urllib3==1.26.2
|
1552 |
+
virtualenv==20.2.1
|
1553 |
+
wandb==0.12.6
|
1554 |
+
wcwidth==0.2.5
|
1555 |
+
webdataset==0.1.62
|
1556 |
+
webencodings==0.5.1
|
1557 |
+
Werkzeug==1.0.1
|
1558 |
+
wget==3.2
|
1559 |
+
widgetsnbextension==3.5.1
|
1560 |
+
wordninja==2.0.0
|
1561 |
+
wrapt==1.12.1
|
1562 |
+
xmltodict==0.13.0
|
1563 |
+
xxhash==2.0.0
|
1564 |
+
yamllint==1.23.0
|
1565 |
+
yarg==0.1.9
|
1566 |
+
yarl==1.7.2
|
1567 |
+
yaspin==2.1.0
|
1568 |
+
youtokentome==1.0.6
|
1569 |
+
youtube-dl==2021.6.6
|
1570 |
+
zipp==3.6.0
|
1571 |
+
|
1572 |
+
|
1573 |
+
2023-01-07 15:59:54,960 - speechbrain.dataio.encoder - DEBUG - Loaded categorical encoding from partly_frozen_splitted_wavlm/save/label_encoder.txt
|
1574 |
+
2023-01-07 15:59:54,960 - speechbrain.dataio.encoder - INFO - Load called, but CTCTextEncoder is not empty. Loaded data will overwrite everything. This is normal if there is e.g. an unk label defined at init.
|
1575 |
+
2023-01-07 15:59:54,961 - speechbrain.dataio.encoder - DEBUG - Loaded categorical encoding from partly_frozen_splitted_wavlm/save/label_encoder.txt
|
1576 |
+
2023-01-07 15:59:54,961 - speechbrain.core - INFO - Info: auto_mix_prec arg from hparam file is used
|
1577 |
+
2023-01-07 15:59:54,961 - speechbrain.core - INFO - Info: ckpt_interval_minutes arg from hparam file is used
|
1578 |
+
2023-01-07 15:59:56,396 - speechbrain.core - INFO - 313.4M trainable parameters in ASR
|
1579 |
+
2023-01-07 15:59:56,398 - speechbrain.utils.checkpoints - INFO - Would load a checkpoint here, but none found yet.
|
1580 |
+
2023-01-07 15:59:56,398 - speechbrain.utils.epoch_loop - INFO - Going into epoch 1
|
1581 |
+
2023-01-07 15:59:57,755 - speechbrain.core - ERROR - Exception:
|
1582 |
+
Traceback (most recent call last):
|
1583 |
+
File "ctc_train.py", line 322, in <module>
|
1584 |
+
asr_brain.fit(
|
1585 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/speechbrain/core.py", line 1153, in fit
|
1586 |
+
self._fit_train(train_set=train_set, epoch=epoch, enable=enable)
|
1587 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/speechbrain/core.py", line 1009, in _fit_train
|
1588 |
+
loss = self.fit_batch(batch)
|
1589 |
+
File "ctc_train.py", line 92, in fit_batch
|
1590 |
+
self.wav2vec_optimizer.step()
|
1591 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/torch/optim/optimizer.py", line 140, in wrapper
|
1592 |
+
out = func(*args, **kwargs)
|
1593 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/torch/optim/optimizer.py", line 23, in _use_grad
|
1594 |
+
ret = func(self, *args, **kwargs)
|
1595 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/torch/optim/adam.py", line 218, in step
|
1596 |
+
state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
|
1597 |
+
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 5.80 GiB total capacity; 3.86 GiB already allocated; 64.50 MiB free; 3.93 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
|
1598 |
+
2023-01-07 16:00:47,395 - speechbrain.core - INFO - Beginning experiment!
|
1599 |
+
2023-01-07 16:00:47,395 - speechbrain.core - INFO - Experiment folder: partly_frozen_splitted_wavlm
|
1600 |
+
2023-01-07 16:00:48,055 - speechbrain.utils.superpowers - DEBUG - abkhazia==1.0
|
1601 |
+
absl-py==0.11.0
|
1602 |
+
aiohttp==3.8.0
|
1603 |
+
aiosignal==1.2.0
|
1604 |
+
alabaster==0.7.12
|
1605 |
+
alembic==1.7.4
|
1606 |
+
altgraph==0.17
|
1607 |
+
antlr4-python3-runtime==4.8
|
1608 |
+
appdirs==1.4.4
|
1609 |
+
argcomplete==1.12.2
|
1610 |
+
argon2-cffi==20.1.0
|
1611 |
+
asgiref==3.6.0
|
1612 |
+
astunparse==1.6.3
|
1613 |
+
async-generator==1.10
|
1614 |
+
async-timeout==4.0.0
|
1615 |
+
attrdict==2.0.1
|
1616 |
+
attrs==20.3.0
|
1617 |
+
audeer==1.16.0
|
1618 |
+
audformat==0.11.5
|
1619 |
+
audinterface==0.7.0
|
1620 |
+
audiofile==1.0.0
|
1621 |
+
audiomentations==0.25.0
|
1622 |
+
audioread==2.1.9
|
1623 |
+
audobject==0.4.14
|
1624 |
+
audresample==0.1.6
|
1625 |
+
-e git+https://github.com/facebookresearch/WavAugment.git@54afcdb00ccc852c2f030f239f8532c9562b550e#egg=augment
|
1626 |
+
autopage==0.4.0
|
1627 |
+
Babel==2.9.0
|
1628 |
+
backcall==0.2.0
|
1629 |
+
beautifulsoup4==4.10.0
|
1630 |
+
black==19.10b0
|
1631 |
+
bleach==3.3.0
|
1632 |
+
boto3==1.20.2
|
1633 |
+
botocore==1.23.2
|
1634 |
+
braceexpand==0.1.7
|
1635 |
+
cachetools==4.2.0
|
1636 |
+
certifi @ file:///croot/certifi_1671487769961/work/certifi
|
1637 |
+
cffi==1.14.3
|
1638 |
+
cfgv==3.2.0
|
1639 |
+
chardet==3.0.4
|
1640 |
+
charset-normalizer==2.0.7
|
1641 |
+
click==7.1.2
|
1642 |
+
cliff==3.9.0
|
1643 |
+
clldutils==3.5.4
|
1644 |
+
cmaes==0.8.2
|
1645 |
+
cmake==3.18.4.post1
|
1646 |
+
cmd2==2.2.0
|
1647 |
+
colorama==0.4.4
|
1648 |
+
colorlog==4.6.2
|
1649 |
+
configparser==5.1.0
|
1650 |
+
cryptography==38.0.4
|
1651 |
+
csvw==1.8.1
|
1652 |
+
cycler==0.10.0
|
1653 |
+
Cython==0.29.21
|
1654 |
+
dataclasses==0.6
|
1655 |
+
datasets==1.5.0
|
1656 |
+
decorator==4.4.2
|
1657 |
+
deepspeech==0.9.1
|
1658 |
+
defusedxml==0.7.1
|
1659 |
+
denoiser==0.1.5
|
1660 |
+
dill==0.3.3
|
1661 |
+
Distance==0.1.3
|
1662 |
+
distlib==0.3.1
|
1663 |
+
Django==3.2.16
|
1664 |
+
django-auditlog==2.2.1
|
1665 |
+
django-filter==22.1
|
1666 |
+
django-js-asset==1.2.2
|
1667 |
+
django-mptt==0.14.0
|
1668 |
+
djangorestframework==3.14.0
|
1669 |
+
docker-pycreds==0.4.0
|
1670 |
+
docopt==0.6.2
|
1671 |
+
docutils==0.16
|
1672 |
+
drf-excel==2.2.0
|
1673 |
+
drf-flex-fields==1.0.0
|
1674 |
+
drf-renderer-xlsx==0.4.1
|
1675 |
+
easyocr==1.2.1
|
1676 |
+
editdistance==0.6.0
|
1677 |
+
emoji==2.2.0
|
1678 |
+
entrypoints==0.3
|
1679 |
+
et-xmlfile==1.1.0
|
1680 |
+
exceptiongroup==1.1.0
|
1681 |
+
farasapy==0.0.14
|
1682 |
+
fasttext==0.9.2
|
1683 |
+
ffmpeg-python==0.2.0
|
1684 |
+
filelock==3.0.12
|
1685 |
+
flake8==3.7.9
|
1686 |
+
flatbuffers==1.12
|
1687 |
+
frozendict==2.0.7
|
1688 |
+
frozenlist==1.2.0
|
1689 |
+
fsspec==2021.11.0
|
1690 |
+
future==0.18.2
|
1691 |
+
g2p-en==2.1.0
|
1692 |
+
gast==0.3.3
|
1693 |
+
gdown==4.2.0
|
1694 |
+
gensim==4.0.1
|
1695 |
+
gitdb==4.0.9
|
1696 |
+
GitPython==3.1.24
|
1697 |
+
google-auth==1.24.0
|
1698 |
+
google-auth-oauthlib==0.4.2
|
1699 |
+
google-pasta==0.2.0
|
1700 |
+
greenlet==1.1.2
|
1701 |
+
grpcio==1.32.0
|
1702 |
+
h5features==1.3.2
|
1703 |
+
h5py==2.10.0
|
1704 |
+
htk-io==0.5
|
1705 |
+
huggingface-hub==0.9.1
|
1706 |
+
hydra-colorlog==0.1.4
|
1707 |
+
hydra-core==0.11.3
|
1708 |
+
HyperPyYAML==1.1.0
|
1709 |
+
hypothesis==6.61.2
|
1710 |
+
identify==1.5.10
|
1711 |
+
idna==2.10
|
1712 |
+
imageio==2.9.0
|
1713 |
+
imagesize==1.2.0
|
1714 |
+
importlib-metadata==4.8.1
|
1715 |
+
importlib-resources==5.2.2
|
1716 |
+
inflect==5.3.0
|
1717 |
+
ipadic==1.0.0
|
1718 |
+
ipykernel==5.3.4
|
1719 |
+
ipython==7.19.0
|
1720 |
+
ipython-genutils==0.2.0
|
1721 |
+
ipywidgets==7.6.3
|
1722 |
+
iso-639==0.4.5
|
1723 |
+
isodate==0.6.0
|
1724 |
+
isort==4.3.21
|
1725 |
+
jedi==0.17.2
|
1726 |
+
jieba==0.42.1
|
1727 |
+
Jinja2==2.11.2
|
1728 |
+
jiwer==2.2.0
|
1729 |
+
jmespath==0.10.0
|
1730 |
+
joblib==0.17.0
|
1731 |
+
jsonschema==3.2.0
|
1732 |
+
julius==0.2.7
|
1733 |
+
jupyter-client==6.1.7
|
1734 |
+
jupyter-core==4.7.0
|
1735 |
+
jupyterlab-pygments==0.1.2
|
1736 |
+
jupyterlab-widgets==1.0.0
|
1737 |
+
kaitaistruct==0.9
|
1738 |
+
kaldi-io==0.9.4
|
1739 |
+
kaldi-python-io==1.2.2
|
1740 |
+
kaldiio==2.17.2
|
1741 |
+
kenlm @ https://github.com/kpu/kenlm/archive/master.zip
|
1742 |
+
Keras-Preprocessing==1.1.2
|
1743 |
+
kiwisolver==1.3.1
|
1744 |
+
lang-trans==0.6.0
|
1745 |
+
latexcodec==2.0.1
|
1746 |
+
ldap3==2.9.1
|
1747 |
+
librosa==0.9.0
|
1748 |
+
llvmlite==0.35.0
|
1749 |
+
lxml==4.9.0
|
1750 |
+
Mako==1.1.5
|
1751 |
+
Markdown==3.3.3
|
1752 |
+
MarkupSafe==1.1.1
|
1753 |
+
marshmallow==3.14.0
|
1754 |
+
matplotlib==3.3.3
|
1755 |
+
mccabe==0.6.1
|
1756 |
+
mcd==0.4
|
1757 |
+
mecab-python3==1.0.3
|
1758 |
+
megatron-lm==2.2.0
|
1759 |
+
mido==1.2.10
|
1760 |
+
mistune==0.8.4
|
1761 |
+
more-itertools==8.6.0
|
1762 |
+
mpmath==1.2.1
|
1763 |
+
multidict==5.2.0
|
1764 |
+
multiprocess==0.70.11.1
|
1765 |
+
nbclient==0.5.3
|
1766 |
+
nbconvert==6.0.7
|
1767 |
+
nbformat==5.1.3
|
1768 |
+
NEMO==4.3.2
|
1769 |
+
nemo-toolkit==1.4.0
|
1770 |
+
nest-asyncio==1.5.1
|
1771 |
+
networkx==2.5
|
1772 |
+
nltk==3.5
|
1773 |
+
nodeenv==1.5.0
|
1774 |
+
notebook==6.3.0
|
1775 |
+
numba==0.52.0
|
1776 |
+
numpy==1.19.4
|
1777 |
+
nvidia-cublas-cu11==11.10.3.66
|
1778 |
+
nvidia-cuda-nvrtc-cu11==11.7.99
|
1779 |
+
nvidia-cuda-runtime-cu11==11.7.99
|
1780 |
+
nvidia-cudnn-cu11==8.5.0.96
|
1781 |
+
oauthlib==3.1.0
|
1782 |
+
omegaconf==1.4.1
|
1783 |
+
onnx==1.10.2
|
1784 |
+
OpenCC==1.1.2
|
1785 |
+
opencv-python==4.4.0.46
|
1786 |
+
openpyxl==3.0.9
|
1787 |
+
opensmile==2.2.0
|
1788 |
+
opt-einsum==3.3.0
|
1789 |
+
optuna==2.10.0
|
1790 |
+
oyaml==1.0
|
1791 |
+
packaging==22.0
|
1792 |
+
pandas==1.2.5
|
1793 |
+
pandocfilters==1.4.3
|
1794 |
+
pangu==4.0.6.1
|
1795 |
+
parameterized==0.8.1
|
1796 |
+
parso==0.7.1
|
1797 |
+
pathspec==0.8.1
|
1798 |
+
pathtools==0.1.2
|
1799 |
+
pbr==5.6.0
|
1800 |
+
pefile==2019.4.18
|
1801 |
+
pescador==2.1.0
|
1802 |
+
pesq==0.0.3
|
1803 |
+
pexpect==4.8.0
|
1804 |
+
phonemizer==2.2.1
|
1805 |
+
pickleshare==0.7.5
|
1806 |
+
Pillow==9.3.0
|
1807 |
+
pip-api==0.0.23
|
1808 |
+
pipreqs==0.4.11
|
1809 |
+
pluggy==0.13.1
|
1810 |
+
pooch==1.3.0
|
1811 |
+
portalocker==2.3.2
|
1812 |
+
pre-commit==2.9.0
|
1813 |
+
pretty-midi==0.2.9
|
1814 |
+
prettytable==2.2.1
|
1815 |
+
progressbar2==3.53.1
|
1816 |
+
prometheus-client==0.10.1
|
1817 |
+
promise==2.3
|
1818 |
+
prompt-toolkit==3.0.8
|
1819 |
+
protobuf==3.14.0
|
1820 |
+
psutil==5.6.6
|
1821 |
+
ptyprocess==0.6.0
|
1822 |
+
py==1.9.0
|
1823 |
+
py-espeak-ng==0.1.8
|
1824 |
+
pyannote.audio==1.1.1
|
1825 |
+
pyannote.core==4.3
|
1826 |
+
pyannote.database==4.1.1
|
1827 |
+
pyannote.metrics==3.1
|
1828 |
+
pyannote.pipeline==1.5.2
|
1829 |
+
PyArabic==0.6.15
|
1830 |
+
pyarrow==3.0.0
|
1831 |
+
pyasn1==0.4.8
|
1832 |
+
pyasn1-modules==0.2.8
|
1833 |
+
pybind11==2.8.1
|
1834 |
+
pybtex==0.24.0
|
1835 |
+
pybtex-docutils==1.0.1
|
1836 |
+
pycodestyle==2.5.0
|
1837 |
+
pycparser==2.20
|
1838 |
+
pyctcdecode==0.4.0
|
1839 |
+
pyDeprecate==0.3.1
|
1840 |
+
pydub==0.25.1
|
1841 |
+
pyflakes==2.1.1
|
1842 |
+
Pygments==2.7.2
|
1843 |
+
pygtrie==2.5.0
|
1844 |
+
pymodbus==2.5.3
|
1845 |
+
pyparsing==2.4.7
|
1846 |
+
pyperclip==1.8.2
|
1847 |
+
pypinyin==0.43.0
|
1848 |
+
pyrsistent==0.17.3
|
1849 |
+
pyserial==3.5
|
1850 |
+
PySocks==1.7.1
|
1851 |
+
pystoi==0.3.3
|
1852 |
+
pytest==5.4.1
|
1853 |
+
pytest-runner==5.3.1
|
1854 |
+
python-bidi==0.4.2
|
1855 |
+
python-crfsuite==0.9.7
|
1856 |
+
python-dateutil==2.8.2
|
1857 |
+
python-Levenshtein==0.12.2
|
1858 |
+
python-utils==2.4.0
|
1859 |
+
pytorch-lightning==1.4.9
|
1860 |
+
pytube==11.0.1
|
1861 |
+
pytz==2022.6
|
1862 |
+
PyWavelets==1.1.1
|
1863 |
+
PyYAML==5.3.1
|
1864 |
+
pyzmq==20.0.0
|
1865 |
+
rapidfuzz==1.8.2
|
1866 |
+
regex==2020.11.13
|
1867 |
+
requests==2.28.1
|
1868 |
+
requests-oauthlib==1.3.0
|
1869 |
+
resampy==0.2.2
|
1870 |
+
rfc3986==1.4.0
|
1871 |
+
rsa==4.7
|
1872 |
+
ruamel.yaml==0.17.21
|
1873 |
+
ruamel.yaml.clib==0.2.7
|
1874 |
+
s3m==1.1.0
|
1875 |
+
s3transfer==0.5.0
|
1876 |
+
sacrebleu==2.0.0
|
1877 |
+
sacremoses==0.0.44
|
1878 |
+
scikit-image==0.18.1
|
1879 |
+
scikit-learn==0.23.2
|
1880 |
+
scipy==1.5.4
|
1881 |
+
-e git+https://github.com/sanghack81/SDCIT@00d060dde733fde9345154a494f81e97fb395ca7#egg=SDCIT
|
1882 |
+
seaborn==0.11.1
|
1883 |
+
segments==2.1.3
|
1884 |
+
Send2Trash==1.5.0
|
1885 |
+
sentencepiece==0.1.94
|
1886 |
+
sentry-sdk==1.4.3
|
1887 |
+
shellingham==1.4.0
|
1888 |
+
shortuuid==1.0.7
|
1889 |
+
SIDEKIT==1.3.8.5.2
|
1890 |
+
simplejson==3.17.5
|
1891 |
+
six==1.15.0
|
1892 |
+
smart-open==5.0.0
|
1893 |
+
smmap==5.0.0
|
1894 |
+
snowballstemmer==2.0.0
|
1895 |
+
sortedcollections==2.1.0
|
1896 |
+
sortedcontainers==2.4.0
|
1897 |
+
sounddevice==0.4.5
|
1898 |
+
SoundFile==0.10.3.post1
|
1899 |
+
soupsieve==2.3
|
1900 |
+
sox==1.4.1
|
1901 |
+
sparsemax==0.1.9
|
1902 |
+
speechbrain==0.5.13
|
1903 |
+
sphfile==1.0.3
|
1904 |
+
Sphinx==3.3.1
|
1905 |
+
sphinx-rtd-theme==0.4.3
|
1906 |
+
sphinxcontrib-applehelp==1.0.2
|
1907 |
+
sphinxcontrib-bibtex==2.4.1
|
1908 |
+
sphinxcontrib-devhelp==1.0.2
|
1909 |
+
sphinxcontrib-htmlhelp==1.0.3
|
1910 |
+
sphinxcontrib-jsmath==1.0.1
|
1911 |
+
sphinxcontrib-qthelp==1.0.3
|
1912 |
+
sphinxcontrib-serializinghtml==1.1.4
|
1913 |
+
SQLAlchemy==1.4.25
|
1914 |
+
sqlparse==0.4.2
|
1915 |
+
stanza==1.4.2
|
1916 |
+
stevedore==3.4.0
|
1917 |
+
subprocess32==3.5.4
|
1918 |
+
sympy==1.9
|
1919 |
+
tabulate==0.8.9
|
1920 |
+
tensorboard==2.4.0
|
1921 |
+
tensorboard-plugin-wit==1.7.0
|
1922 |
+
tensorflow==2.4.0
|
1923 |
+
tensorflow-estimator==2.4.0
|
1924 |
+
termcolor==1.1.0
|
1925 |
+
terminado==0.9.4
|
1926 |
+
testpath==0.4.4
|
1927 |
+
threadpoolctl==2.1.0
|
1928 |
+
tifffile==2020.12.8
|
1929 |
+
tikzplotlib==0.9.8
|
1930 |
+
tkseem==0.0.3
|
1931 |
+
tokenizers==0.10.2
|
1932 |
+
toml==0.10.2
|
1933 |
+
torch==1.13.1
|
1934 |
+
torch-stft==0.1.4
|
1935 |
+
torchaudio==0.13.1
|
1936 |
+
torchmetrics==0.6.0
|
1937 |
+
torchvision==0.14.1
|
1938 |
+
tornado==6.1
|
1939 |
+
tqdm==4.61.1
|
1940 |
+
trackrip==1.2.1
|
1941 |
+
traitlets==5.0.5
|
1942 |
+
transformers==4.15.0
|
1943 |
+
typed-ast==1.4.1
|
1944 |
+
typer==0.4.0
|
1945 |
+
typing-extensions==3.7.4.3
|
1946 |
+
Unidecode==1.3.2
|
1947 |
+
uritemplate==3.0.1
|
1948 |
+
urllib3==1.26.2
|
1949 |
+
virtualenv==20.2.1
|
1950 |
+
wandb==0.12.6
|
1951 |
+
wcwidth==0.2.5
|
1952 |
+
webdataset==0.1.62
|
1953 |
+
webencodings==0.5.1
|
1954 |
+
Werkzeug==1.0.1
|
1955 |
+
wget==3.2
|
1956 |
+
widgetsnbextension==3.5.1
|
1957 |
+
wordninja==2.0.0
|
1958 |
+
wrapt==1.12.1
|
1959 |
+
xmltodict==0.13.0
|
1960 |
+
xxhash==2.0.0
|
1961 |
+
yamllint==1.23.0
|
1962 |
+
yarg==0.1.9
|
1963 |
+
yarl==1.7.2
|
1964 |
+
yaspin==2.1.0
|
1965 |
+
youtokentome==1.0.6
|
1966 |
+
youtube-dl==2021.6.6
|
1967 |
+
zipp==3.6.0
|
1968 |
+
|
1969 |
+
|
1970 |
+
2023-01-07 16:00:48,174 - speechbrain.dataio.encoder - DEBUG - Loaded categorical encoding from partly_frozen_splitted_wavlm/save/label_encoder.txt
|
1971 |
+
2023-01-07 16:00:48,174 - speechbrain.dataio.encoder - INFO - Load called, but CTCTextEncoder is not empty. Loaded data will overwrite everything. This is normal if there is e.g. an unk label defined at init.
|
1972 |
+
2023-01-07 16:00:48,175 - speechbrain.dataio.encoder - DEBUG - Loaded categorical encoding from partly_frozen_splitted_wavlm/save/label_encoder.txt
|
1973 |
+
2023-01-07 16:00:48,176 - speechbrain.core - INFO - Info: auto_mix_prec arg from hparam file is used
|
1974 |
+
2023-01-07 16:00:48,176 - speechbrain.core - INFO - Info: ckpt_interval_minutes arg from hparam file is used
|
1975 |
+
2023-01-07 16:00:49,674 - speechbrain.core - INFO - 313.4M trainable parameters in ASR
|
1976 |
+
2023-01-07 16:00:50,298 - speechbrain.utils.checkpoints - INFO - Would load a checkpoint here, but none found yet.
|
1977 |
+
2023-01-07 16:00:50,298 - speechbrain.utils.epoch_loop - INFO - Going into epoch 1
|
1978 |
+
2023-01-07 16:01:08,641 - speechbrain.core - ERROR - Exception:
|
1979 |
+
Traceback (most recent call last):
|
1980 |
+
File "ctc_train.py", line 324, in <module>
|
1981 |
+
asr_brain.fit(
|
1982 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/speechbrain/core.py", line 1153, in fit
|
1983 |
+
self._fit_train(train_set=train_set, epoch=epoch, enable=enable)
|
1984 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/speechbrain/core.py", line 1009, in _fit_train
|
1985 |
+
loss = self.fit_batch(batch)
|
1986 |
+
File "ctc_train.py", line 92, in fit_batch
|
1987 |
+
self.wav2vec_optimizer.step()
|
1988 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/torch/optim/optimizer.py", line 140, in wrapper
|
1989 |
+
out = func(*args, **kwargs)
|
1990 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/torch/optim/optimizer.py", line 23, in _use_grad
|
1991 |
+
ret = func(self, *args, **kwargs)
|
1992 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/torch/optim/adam.py", line 234, in step
|
1993 |
+
adam(params_with_grad,
|
1994 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/torch/optim/adam.py", line 300, in adam
|
1995 |
+
func(params,
|
1996 |
+
File "/home/salah/anaconda3/envs/salah/lib/python3.8/site-packages/torch/optim/adam.py", line 410, in _single_tensor_adam
|
1997 |
+
denom = (exp_avg_sq.sqrt() / bias_correction2_sqrt).add_(eps)
|
1998 |
+
KeyboardInterrupt
|
partly_frozen_splitted_wavlm/save/label_encoder.txt
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
'ت' => 29
|
2 |
+
'ع' => 30
|
3 |
+
'ب' => 31
|
4 |
+
' ' => 3
|
5 |
+
'ه' => 4
|
6 |
+
'ا' => 5
|
7 |
+
'ن' => 6
|
8 |
+
'ي' => 7
|
9 |
+
'ر' => 8
|
10 |
+
'ك' => 9
|
11 |
+
'ش' => 10
|
12 |
+
'ف' => 11
|
13 |
+
'ل' => 12
|
14 |
+
'د' => 13
|
15 |
+
'س' => 14
|
16 |
+
'م' => 15
|
17 |
+
'ق' => 16
|
18 |
+
'ى' => 17
|
19 |
+
'ء' => 18
|
20 |
+
'و' => 19
|
21 |
+
'ح' => 20
|
22 |
+
'ز' => 21
|
23 |
+
'ة' => 22
|
24 |
+
'أ' => 23
|
25 |
+
'خ' => 24
|
26 |
+
'ص' => 25
|
27 |
+
'ط' => 26
|
28 |
+
'ج' => 27
|
29 |
+
'ظ' => 28
|
30 |
+
'<blank>' => 0
|
31 |
+
'<bos>' => 1
|
32 |
+
'<eos>' => 2
|
33 |
+
================
|
34 |
+
'starting_index' => 0
|
35 |
+
'bos_label' => '<bos>'
|
36 |
+
'eos_label' => '<eos>'
|
37 |
+
'blank_label' => '<blank>'
|
recording.webm
ADDED
Binary file (48.8 kB). View file
|
|
requirements.txt
ADDED
@@ -0,0 +1,16 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
-r lint-requirements.txt
|
2 |
+
huggingface_hub>=0.7.0
|
3 |
+
hyperpyyaml>=0.0.1
|
4 |
+
joblib>=0.14.1
|
5 |
+
numpy>=1.17.0
|
6 |
+
packaging
|
7 |
+
pre-commit>=2.3.0
|
8 |
+
scipy>=1.4.1, <1.9
|
9 |
+
sentencepiece>=0.1.91
|
10 |
+
SoundFile; sys_platform == 'win32'
|
11 |
+
torch>=1.9.0
|
12 |
+
torchaudio>=0.9.0
|
13 |
+
tqdm>=4.42.0
|
14 |
+
transformers==4.15
|
15 |
+
speechbrain
|
16 |
+
pyctcdecode
|
running_tunisian.ipynb
ADDED
The diff for this file is too large to render.
See raw diff
|
|
samples/Salah1.wav
ADDED
Binary file (952 kB). View file
|
|
samples/Salah10.wav
ADDED
Binary file (768 kB). View file
|
|