70 lines
2.2 KiB
Markdown
70 lines
2.2 KiB
Markdown
---
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language: en
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tags:
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- audio
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- automatic-speech-recognition
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- voxpopuli
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license: cc-by-nc-4.0
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---
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# Wav2Vec2-Base-VoxPopuli-Finetuned
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[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in en (refer to Table 1 of paper for more information).
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**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
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Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
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**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*
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See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/)
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# Usage for inference
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In the following it is shown how the model can be used in inference on a sample of the [Common Voice dataset](https://commonvoice.mozilla.org/en/datasets)
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```python
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#!/usr/bin/env python3
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from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
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from datasets import load_dataset
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import torchaudio
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import torch
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# resample audio
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# load model & processor
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model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-10k-voxpopuli-ft-en")
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processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-10k-voxpopuli-ft-en")
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# load dataset
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ds = load_dataset("common_voice", "en", split="validation[:1%]")
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# common voice does not match target sampling rate
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common_voice_sample_rate = 48000
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target_sample_rate = 16000
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resampler = torchaudio.transforms.Resample(common_voice_sample_rate, target_sample_rate)
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# define mapping fn to read in sound file and resample
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def map_to_array(batch):
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speech, _ = torchaudio.load(batch["path"])
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speech = resampler(speech)
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batch["speech"] = speech[0]
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return batch
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# load all audio files
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ds = ds.map(map_to_array)
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# run inference on the first 5 data samples
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inputs = processor(ds[:5]["speech"], sampling_rate=target_sample_rate, return_tensors="pt", padding=True)
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# inference
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logits = model(**inputs).logits
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predicted_ids = torch.argmax(logits, axis=-1)
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print(processor.batch_decode(predicted_ids))
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```
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