Wav2vec2 Base 10k Voxpopuli Ft En

🚀 Wav2Vec2-Base-VoxPopuli-Finetuned

This project is based on Facebook's Wav2Vec2. The base model is pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in English. For more information, refer to Table 1 of the paper.

✨ Features

• The model is based on the pre - trained Wav2Vec2 base model, leveraging the knowledge learned from the large - scale unlabeled data of the VoxPopuli corpus.
• Fine - tuned on English transcribed data, making it suitable for English automatic speech recognition tasks.

📄 License

The model is released under the CC - BY - NC - 4.0 license.

💻 Usage Examples

Basic Usage

#!/usr/bin/env python3
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torchaudio
import torch

# resample audio

# load model & processor
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-10k-voxpopuli-ft-en")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-10k-voxpopuli-ft-en")

# load dataset
ds = load_dataset("common_voice", "en", split="validation[:1%]")

# common voice does not match target sampling rate
common_voice_sample_rate = 48000
target_sample_rate = 16000

resampler = torchaudio.transforms.Resample(common_voice_sample_rate, target_sample_rate)


# define mapping fn to read in sound file and resample
def map_to_array(batch):
    speech, _ = torchaudio.load(batch["path"])
    speech = resampler(speech)
    batch["speech"] = speech[0]
    return batch


# load all audio files
ds = ds.map(map_to_array)

# run inference on the first 5 data samples
inputs = processor(ds[:5]["speech"], sampling_rate=target_sample_rate, return_tensors="pt", padding=True)

# inference
logits = model(**inputs).logits
predicted_ids = torch.argmax(logits, axis=-1)

print(processor.batch_decode(predicted_ids))

📚 Documentation

Paper: VoxPopuli: A Large - Scale Multilingual Speech Corpus for Representation Learning, Semi - Supervised Learning and Interpretation

Authors: Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux from Facebook AI

See the official website for more information, here