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--- |
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license: mit |
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language: |
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- ru |
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pipeline_tag: automatic-speech-recognition |
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library_name: transformers |
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tags: |
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- asr |
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- gigaam |
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- stt |
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- audio |
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- speech |
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- rnnt |
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- transducer |
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--- |
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# GigaAM-v2-CTC 🤗 Hugging Face transformers |
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* original git https://github.com/salute-developers/GigaAM |
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Russian ASR model GigaAM-v2-RNNT. |
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## Model info |
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This is an original GigaAM-v2-RNNT with `transformers` library interface. |
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File [`gigaam_transformers.py`](https://huggingface.co/waveletdeboshir/gigaam-rnnt/blob/main/gigaam_transformers.py) contains model, feature extractor and tokenizer classes with usual transformers methods. Model can be initialized with transformers auto classes (see an example below). |
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## Installation |
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my lib versions: |
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* `torch` 2.5.1 |
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* `torchaudio` 2.5.1 |
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* `transformers` 4.49.0 |
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## Usage |
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Usage is same as other `transformers` ASR models. |
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```python |
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from transformers import AutoModel, AutoProcessor |
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import torch |
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import torchaudio |
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# load audio |
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wav, sr = torchaudio.load("audio.wav") |
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# resample if necessary |
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wav = torchaudio.functional.resample(wav, sr, 16000) |
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# load model and processor |
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processor = AutoProcessor.from_pretrained("waveletdeboshir/gigaam-rnnt", trust_remote_code=True) |
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model = AutoModel.from_pretrained("waveletdeboshir/gigaam-rnnt", trust_remote_code=True) |
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model.eval() |
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input_features = processor(wav[0], sampling_rate=16000, return_tensors="pt") |
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# greedy prediction |
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with torch.no_grad(): |
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pred_ids = model.generate(**input_features) |
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# decode token ids to text |
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transcription = processor.batch_decode(pred_ids)[0] |
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``` |
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## Fine-tune |
