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--- |
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language: hi |
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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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- wav2vec2 |
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- asr |
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license: apache-2.0 |
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--- |
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# IndicWav2Vec-Hindi |
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This is a [Wav2Vec2](https://arxiv.org/abs/2006.11477) style ASR model trained in [fairseq](https://github.com/facebookresearch/fairseq) and ported to Hugging Face. |
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More details on datasets, training-setup and conversion to HuggingFace format can be found in the [IndicWav2Vec](https://github.com/AI4Bharat/IndicWav2Vec) repo. |
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*Note: This model doesn't support inference with Language Model.* |
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## Script to Run Inference |
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```python |
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import torch |
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from datasets import load_dataset |
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from transformers import AutoModelForCTC, AutoProcessor |
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import torchaudio.functional as F |
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DEVICE_ID = "cuda" if torch.cuda.is_available() else "cpu" |
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MODEL_ID = "ai4bharat/indicwav2vec-hindi" |
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sample = next(iter(load_dataset("common_voice", "hi", split="test", streaming=True))) |
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resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48000, 16000).numpy() |
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model = AutoModelForCTC.from_pretrained(MODEL_ID).to(DEVICE_ID) |
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processor = AutoProcessor.from_pretrained(MODEL_ID) |
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input_values = processor(resampled_audio, return_tensors="pt").input_values |
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with torch.no_grad(): |
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logits = model(input_values.to(DEVICE_ID)).logits.cpu() |
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prediction_ids = torch.argmax(logits, dim=-1) |
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output_str = processor.batch_decode(prediction_ids)[0] |
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print(f"Greedy Decoding: {output_str}") |
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``` |
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# **About AI4Bharat** |
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- Website: https://ai4bharat.org/ |
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- Code: https://github.com/AI4Bharat |
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- HuggingFace: https://huggingface.co/ai4bharat |
