Wav2vec2 Base 10k Voxpopuli Ft Sk

🚀 Wav2Vec2-Base-VoxPopuli-Finetuned

This is a base model of Facebook's Wav2Vec2. It was pre - trained on the 10K unlabeled subset of the VoxPopuli corpus and fine - tuned on the transcribed data in Slovak (refer to Table 1 of the paper for more information).

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

🚀 Quick Start

This section provides a high - level overview of the model and its purpose.

✨ Features

• Based on Facebook's Wav2Vec2 base model.
• Pretrained on the 10K unlabeled subset of the VoxPopuli corpus.
• Finetuned on Slovak transcribed data.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 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-sk")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-10k-voxpopuli-ft-sk")

# load dataset
ds = load_dataset("common_voice", "sk", 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))

Advanced Usage

There is no advanced usage example in the original document, so this part is skipped.

📚 Documentation

The model's documentation can be found in the official paper and website.

📄 License

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