jonatasgrosman
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Browse files- README.md +157 -0
- config.json +69 -0
- preprocessor_config.json +8 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- vocab.json +1 -0
README.md
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---
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language: fr
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datasets:
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- common_voice
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metrics:
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- wer
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- cer
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tags:
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- audio
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- automatic-speech-recognition
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- speech
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- xlsr-fine-tuning-week
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license: apache-2.0
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model-index:
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- name: XLSR Wav2Vec2 French by Jonatas Grosman
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results:
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- task:
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name: Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: Common Voice fr
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type: common_voice
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args: fr
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metrics:
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- name: Test WER
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type: wer
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value: 18.01
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- name: Test CER
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type: cer
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value: 6.29
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---
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# Wav2Vec2-Large-XLSR-53-French
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on French using the [Common Voice](https://huggingface.co/datasets/common_voice) and [CSS10](https://github.com/Kyubyong/css10).
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When using this model, make sure that your speech input is sampled at 16kHz.
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The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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## Usage
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The model can be used directly (without a language model) as follows:
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```python
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import torch
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import librosa
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from datasets import load_dataset
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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LANG_ID = "fr"
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MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-french"
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SAMPLES = 5
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test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
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processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
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model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
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# Preprocessing the datasets.
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# We need to read the audio files as arrays
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def speech_file_to_array_fn(batch):
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speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
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batch["speech"] = speech_array
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batch["sentence"] = batch["sentence"].upper()
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return batch
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test_dataset = test_dataset.map(speech_file_to_array_fn)
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inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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with torch.no_grad():
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logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
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predicted_ids = torch.argmax(logits, dim=-1)
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predicted_sentences = processor.batch_decode(predicted_ids)
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for i, predicted_sentence in enumerate(predicted_sentences):
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print("-" * 100)
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print("Reference:", test_dataset[i]["sentence"])
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print("Prediction:", predicted_sentence)
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```
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| Reference | Prediction |
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| ------------- | ------------- |
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| "CE DERNIER A ÉVOLUÉ TOUT AU LONG DE L'HISTOIRE ROMAINE." | CE DERNIER EST VOLÉ TOUT AU LONG DE L'HISTOIRE ROMAINE |
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| CE SITE CONTIENT QUATRE TOMBEAUX DE LA DYNASTIE ACHÉMÉNIDE ET SEPT DES SASSANIDES. | CE SITE CONTIENT QUATRE TOMBEAUX DE LA DYNASTIE ASHÉMÉNIDE ET SEPT DES SASANNIDES |
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| "J'AI DIT QUE LES ACTEURS DE BOIS AVAIENT, SELON MOI, BEAUCOUP D'AVANTAGES SUR LES AUTRES." | JAI DIT QUE LES ACTEURS DE BOIS AVAIENT SELON MOI BEAUCOUP DAVANTAGES SUR LES AUTRES |
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| LES PAYS-BAS ONT REMPORTÉ TOUTES LES ÉDITIONS. | LE PAYS-BAS AN REMPORTAIT TOUTES LES ÉDITIONS |
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| IL Y A MAINTENANT UNE GARE ROUTIÈRE. | IL A MA ANDIN GARD DETIRON |
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## Evaluation
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The model can be evaluated as follows on the French test data of Common Voice.
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```python
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import torch
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import re
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import librosa
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from datasets import load_dataset, load_metric
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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LANG_ID = "fr"
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MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-french"
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DEVICE = "cuda"
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MAX_SAMPLES = 8000
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CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
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"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
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"=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。"]
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test_dataset = load_dataset("common_voice", LANG_ID, split="test")
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if len(test_dataset) > MAX_SAMPLES:
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test_dataset = test_dataset.select(range(MAX_SAMPLES))
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wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
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cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py
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chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
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processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
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model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
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model.to(DEVICE)
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# Preprocessing the datasets.
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# We need to read the audio files as arrays
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def speech_file_to_array_fn(batch):
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
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batch["speech"] = speech_array
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batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
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return batch
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test_dataset = test_dataset.map(speech_file_to_array_fn)
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# Preprocessing the datasets.
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# We need to read the audio files as arrays
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def evaluate(batch):
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inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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with torch.no_grad():
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logits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits
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pred_ids = torch.argmax(logits, dim=-1)
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batch["pred_strings"] = processor.batch_decode(pred_ids)
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return batch
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result = test_dataset.map(evaluate, batched=True, batch_size=8)
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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"], chunk_size=1000)))
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print("CER: {:2f}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["sentence"], chunk_size=1000)))
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```
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**Test Result**:
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- WER: 18.01%
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- CER: 6.29%
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config.json
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{
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"_name_or_path": "facebook/wav2vec2-large-xlsr-53",
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"activation_dropout": 0.05,
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"apply_spec_augment": true,
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"architectures": [
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"Wav2Vec2ForCTC"
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],
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"attention_dropout": 0.1,
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"bos_token_id": 1,
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"conv_bias": true,
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"conv_dim": [
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512,
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512,
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512,
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512,
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512,
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512,
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512
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],
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"conv_kernel": [
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10,
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3,
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3,
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2,
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2
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],
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"conv_stride": [
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5,
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],
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"ctc_loss_reduction": "mean",
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"ctc_zero_infinity": true,
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"do_stable_layer_norm": true,
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"eos_token_id": 2,
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"feat_extract_activation": "gelu",
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"feat_extract_dropout": 0.0,
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"feat_extract_norm": "layer",
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"feat_proj_dropout": 0.05,
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"final_dropout": 0.1,
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"gradient_checkpointing": true,
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"hidden_act": "gelu",
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"hidden_dropout": 0.05,
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"layer_norm_eps": 1e-05,
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"layerdrop": 0.05,
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"mask_feature_length": 10,
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"mask_feature_prob": 0.0,
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"mask_time_length": 10,
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"mask_time_prob": 0.05,
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"model_type": "wav2vec2",
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"num_attention_heads": 16,
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"num_conv_pos_embedding_groups": 16,
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"num_conv_pos_embeddings": 128,
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"num_feat_extract_layers": 7,
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"num_hidden_layers": 24,
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"pad_token_id": 0,
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"transformers_version": "4.5.0.dev0",
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"vocab_size": 49
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}
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preprocessor_config.json
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{
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"do_normalize": true,
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"feature_size": 1,
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"padding_side": "right",
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"padding_value": 0.0,
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"return_attention_mask": true,
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"sampling_rate": 16000
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:d3d6ee65a78f8aa34f5355f21586fc77a608aa97c60c231948f7fa19354c1ae5
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size 1262134743
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special_tokens_map.json
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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
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vocab.json
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{"<pad>": 0, "<s>": 1, "</s>": 2, "<unk>": 3, "|": 4, "E": 5, "S": 6, "A": 7, "I": 8, "T": 9, "N": 10, "R": 11, "U": 12, "L": 13, "O": 14, "D": 15, "M": 16, "C": 17, "P": 18, "É": 19, "V": 20, "'": 21, "Q": 22, "F": 23, "G": 24, "B": 25, "H": 26, "J": 27, "À": 28, "X": 29, "È": 30, "Y": 31, "-": 32, "Ê": 33, "Z": 34, "Â": 35, "Ç": 36, "Î": 37, "Ô": 38, "Û": 39, "Ù": 40, "K": 41, "Œ": 42, "Ï": 43, "W": 44, "Ë": 45, "Ü": 46, "Æ": 47, "Ÿ": 48}
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