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whisper-small-ru-pruned-ft
waveletdeboshir/whisper-small-ru-pruned-ft
whisper-small-ru-pruned-ft is a automatic speech recognition model from waveletdeboshir. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.
This is a finetuned version of pruned whisper-small model (waveletdeboshir/whisper-small-ru-pruned) for Russian.
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- apache-2.0
- Model type
- whisper
- Access
- Public
- Created
- Aug 25, 2024
- Updated
- Sep 16, 2024
- SHA
- e752b6a5
Base models
Model card
From the Hugging Face model README
Whisper-small-ru-pruned-ft
Model info
This is a finetuned version of pruned whisper-small model (waveletdeboshir/whisper-small-ru-pruned) for Russian.
Model was finetuned on russian part of mozilla-foundation/common_voice_15_0 with Specaugment, Colored Noise augmentation and Noise from file augmentation.
Metrics
| metric | dataset | waveletdeboshir/whisper-small-ru-pruned | waveletdeboshir/whisper-small-ru-pruned-ft |
|---|---|---|---|
| WER (without punctuation) | common_voice_15_0_test | 0.1748 | 0.1092 |
| WER | common_voice_15_0_test | 0.2492 | 0.1571 |
Limitations
Because texts in Common Voice don't contain digits and other characters except letters and punctuation signs, model lost an ability to predict numbers and special characters.
Size
Only 10% tokens was left including special whisper tokens (no language tokens except <|ru|> and <|en|>, no timestamp tokens), 200 most popular tokens from tokenizer and 4000 most popular Russian tokens computed by tokenization of russian text corpus.
Model size is 15% less then original whisper-small:
| openai/whisper-small | waveletdeboshir/whisper-small-ru-pruned | |
|---|---|---|
| n of parameters | 242 M | 205 M |
| n of parameters (with proj_out layer) | 281 M | 208 M |
| model file size | 967 Mb | 821 Mb |
| vocab_size | 51865 | 4207 |
Usage
Model can be used as an original whisper:
>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> import torchaudio
>>> # load audio
>>> wav, sr = torchaudio.load("audio.wav")
>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("waveletdeboshir/whisper-small-ru-pruned-ft")
>>> model = WhisperForConditionalGeneration.from_pretrained("waveletdeboshir/whisper-small-ru-pruned-ft")
>>> input_features = processor(wav[0], sampling_rate=sr, return_tensors="pt").input_features
>>> # generate token ids
>>> predicted_ids = model.generate(input_features)
>>> # decode token ids to text
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
['<|startoftranscript|><|ru|><|transcribe|><|notimestamps|> Начинаем работу.<|endoftext|>']
The context tokens can be removed from the start of the transcription by setting skip_special_tokens=True.
Other pruned whisper models
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- License
- apache-2.0
- Languages
- ru
- Created
- Aug 25, 2024
- Updated
- Sep 16, 2024