README.md

metadata

library_name: transformers
language:
  - bem
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - BIG-C/BEMBA
metrics:
  - wer
model-index:
  - name: Whisper Small Bemba - Beijuka Bruno
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: BEMBA
          type: BIG-C/BEMBA
          args: 'config: bemba, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 0.4232887826124087

Whisper Small Bemba - Beijuka Bruno

This model is a fine-tuned version of openai/whisper-small on the BEMBA dataset. It achieves the following results on the evaluation set:

• Loss: 1.2836
• Model Preparation Time: 0.0075
• Wer: 0.4233
• Cer: 0.1171

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 4
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Wer	Cer
0.9215	1.0	2546	0.6730	0.0075	0.4818	0.1286
0.5391	2.0	5092	0.6217	0.0075	0.4434	0.1188
0.366	3.0	7638	0.6423	0.0075	0.4449	0.1222
0.2213	4.0	10184	0.6881	0.0075	0.4430	0.1281
0.1152	5.0	12730	0.7444	0.0075	0.4607	0.1329
0.059	6.0	15276	0.8259	0.0075	0.4572	0.1283
0.0336	7.0	17822	0.8798	0.0075	0.4313	0.1186
0.0233	8.0	20368	0.9262	0.0075	0.4321	0.1196
0.0182	9.0	22914	0.9683	0.0075	0.4333	0.1230
0.0159	10.0	25460	0.9992	0.0075	0.4301	0.1215
0.0123	11.0	28006	1.0515	0.0075	0.4279	0.1191
0.0114	12.0	30552	1.0733	0.0075	0.4296	0.1154
0.0105	13.0	33098	1.0854	0.0075	0.4277	0.1171
0.0096	14.0	35644	1.1244	0.0075	0.4228	0.1156
0.0084	15.0	38190	1.1250	0.0075	0.4284	0.1201
0.0082	16.0	40736	1.1691	0.0075	0.4163	0.1142
0.0075	17.0	43282	1.1766	0.0075	0.4172	0.1140
0.0073	18.0	45828	1.1734	0.0075	0.4323	0.1197
0.0067	19.0	48374	1.1985	0.0075	0.4201	0.1170
0.006	20.0	50920	1.2099	0.0075	0.4297	0.1192
0.0057	21.0	53466	1.2517	0.0075	0.4293	0.1197
0.0058	22.0	56012	1.2351	0.0075	0.4292	0.1202
0.0057	23.0	58558	1.2380	0.0075	0.4362	0.1195
0.0048	24.0	61104	1.2756	0.0075	0.4202	0.1165
0.0049	25.0	63650	1.2816	0.0075	0.4185	0.1156
0.0046	26.0	66196	1.3191	0.0075	0.4116	0.1115
0.0041	27.0	68742	1.3041	0.0075	0.4187	0.1160
0.0041	28.0	71288	1.3195	0.0075	0.4193	0.1144
0.0042	29.0	73834	1.3229	0.0075	0.4215	0.1187
0.0033	30.0	76380	1.3282	0.0075	0.4192	0.1159
0.0035	31.0	78926	1.3424	0.0075	0.4256	0.1196
0.0033	32.0	81472	1.3538	0.0075	0.4217	0.1164
0.0072	33.0	84018	1.3673	0.0075	0.4141	0.1148
0.0041	34.0	86564	1.3853	0.0075	0.4195	0.1154
0.0035	35.0	89110	1.3809	0.0075	0.4145	0.1146
0.0034	36.0	91656	1.3930	0.0075	0.4170	0.1158

Framework versions

• Transformers 4.44.2
• Pytorch 2.1.0+cu118
• Datasets 3.0.0
• Tokenizers 0.19.1