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--- |
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language: sl |
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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 sl (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-sl") |
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processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-10k-voxpopuli-ft-sl") |
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# load dataset |
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ds = load_dataset("common_voice", "sl", 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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