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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- dutch |
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- whisper-event |
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metrics: |
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- wer |
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base_model: qmeeus/whisper-small-nl |
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model-index: |
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- name: whisper-small-nl |
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results: [] |
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--- |
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# whisper-small-nl |
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This model is a fine-tuned version of [qmeeus/whisper-small-nl](https://huggingface.co/qmeeus/whisper-small-nl) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3034 |
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- Wer: 14.5354 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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Transcribe files in Dutch: |
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```python |
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import soundfile as sf |
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from transformers import pipeline |
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whisper_asr = pipeline("automatic-speech-recognition", model="qmeeus/whisper-small-nl", device=0) |
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whisper_asr.model.config.forced_decoder_ids = whisper_asr.tokenizer.get_decoder_prompt_ids( |
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task="transcribe", language="nl" |
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) |
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waveform, sr = sf.read(filename) |
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def iter_chunks(waveform, sampling_rate=16_000, chunk_length=30.): |
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assert sampling_rate == 16_000 |
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n_frames = math.floor(sampling_rate * chunk_length) |
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for start in range(0, len(waveform), n_frames): |
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end = min(len(waveform), start + n_frames) |
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yield waveform[start:end] |
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for sentence in whisper_asr(iter_chunks(waveform, sr), max_new_tokens=448): |
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print(sentence["text"]) |
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``` |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 10000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:-----:|:---------------:|:-------:| |
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| 0.2045 | 2.49 | 1000 | 0.3194 | 16.1628 | |
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| 0.0652 | 4.97 | 2000 | 0.3425 | 16.3672 | |
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| 0.0167 | 7.46 | 3000 | 0.3915 | 15.8187 | |
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| 0.0064 | 9.95 | 4000 | 0.4190 | 15.7298 | |
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| 0.1966 | 2.02 | 5000 | 0.3298 | 15.0881 | |
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| 0.1912 | 4.04 | 6000 | 0.3266 | 14.8764 | |
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| 0.1008 | 7.02 | 7000 | 0.3261 | 14.8086 | |
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| 0.0899 | 9.04 | 8000 | 0.3196 | 14.6487 | |
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| 0.1126 | 12.02 | 9000 | 0.3283 | 14.5894 | |
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| 0.1071 | 14.04 | 10000 | 0.3034 | 14.5354 | |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.1.dev0 |
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- Tokenizers 0.13.2 |
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