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Update README.md
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README.md
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- automatic-speech-recognition
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- mozilla-foundation/common_voice_7_0
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- generated_from_trainer
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datasets:
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model-index:
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- name:
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results:
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- Pytorch 1.10.1+cu102
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- Datasets 1.17.1.dev0
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- Tokenizers 0.11.0
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- automatic-speech-recognition
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- mozilla-foundation/common_voice_7_0
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- generated_from_trainer
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- robust-speech-event
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datasets:
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- mozilla-foundation/common_voice_7_0
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metrics:
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- wer
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model-index:
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- name: wav2vec2-xls-r-1b-hi
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results:
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- task:
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type: automatic-speech-recognition
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name: Speech Recognition
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dataset:
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type: mozilla-foundation/common_voice_7_0
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name: Common Voice 7
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args: hi
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metrics:
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- type: wer # Required. Example: wer
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value: 49.972 # Required. Example: 20.90
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name: Test WER # Optional. Example: Test WER
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- name: Test CER
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type: cer
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value: 26.390
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- Pytorch 1.10.1+cu102
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- Datasets 1.17.1.dev0
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- Tokenizers 0.11.0
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#### Evaluation Commands
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1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
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```bash
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python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-or --dataset mozilla-foundation/common_voice_7_0 --config or --split test
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```
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### Inference With LM
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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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model_id = "anuragshas/wav2vec2-xls-r-1b-hi"
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sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "hi", split="test", streaming=True, use_auth_token=True))
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sample = next(sample_iter)
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resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
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model = AutoModelForCTC.from_pretrained(model_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).logits
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transcription = processor.batch_decode(logits.numpy()).text
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# => "तुम्हारे पास तीन महीने बचे हैं"
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```
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### Eval results on Common Voice 7 "test" (WER):
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| Without LM | With LM (run `./eval.py`) |
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|---|---|
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| 60.61 | 49.97 |
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