Wav2vec2 Base 10k Voxpopuli Ft Pl

🚀 Wav2Vec2-Base-VoxPopuli-Finetuned

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

🚀 Quick Start

✨ Features

• Based on the Wav2Vec2 architecture, suitable for automatic speech recognition tasks.
• Fine - tuned on Polish transcribed data, optimized for the Polish language.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

The following code demonstrates how to use the model for inference on a sample of the Common Voice dataset:

#!/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-pl")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-10k-voxpopuli-ft-pl")

# load dataset
ds = load_dataset("common_voice", "pl", 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

📄 License

This model is released under the cc - by - nc - 4.0 license.