54 lines
1.7 KiB
Markdown
54 lines
1.7 KiB
Markdown
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---
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language: zh
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tags:
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- automatic-speech-recognition
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license: cc-by-sa-4.0
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datasets:
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- common_voice
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metrics:
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- cer
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---
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# Wav2vec2-large-xlsr-cantonese
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This model was based on [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53), finetuned using Common Voice/zh-HK/6.1.0.
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The training code is similar to [user ctl](https://huggingface.co/ctl/wav2vec2-large-xlsr-cantonese), except that the number of training epochs was 80 (doubled) and fp16_backend is apex. The model was trained using a single RTX 3090 and docker image is nvidia/cuda:11.1-cudnn8-devel.
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CER is 15.11% when evaluate against common voice zh-HK test set.
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# Result (CER)
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15.11%
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# Source Code
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See this GitHub Repo [cantonese-selfish-project](https://github.com/scottykwok/cantonese-selfish-project/) and [demo video](https://youtu.be/k_9RQ-ilGEc).
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# Usage
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```python
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import soundfile as sf
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import torch
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from datasets import load_dataset
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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# load pretrained model
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processor = Wav2Vec2Processor.from_pretrained("scottykwok/wav2vec2-large-xlsr-cantonese")
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model = Wav2Vec2ForCTC.from_pretrained("scottykwok/wav2vec2-large-xlsr-cantonese")
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# load audio - must be 16kHz mono
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audio_input, sample_rate = sf.read('audio.wav')
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# pad input values and return pt tensor
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input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
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# INFERENCE
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# retrieve logits & take argmax
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logits = model(input_values).logits
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predicted_ids = torch.argmax(logits, dim=-1)
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# transcribe
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transcription = processor.decode(predicted_ids[0])
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print("-" *20)
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print("Transcription:\n", transcription.lower())
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print("-" *20)
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```
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