Wav2vec2 Xls R 1b Polish

🚀 XLS-R Wav2Vec2 Polish by Jonatas Grosman

This is a fine - tuned XLS - R 1B model for Polish speech recognition, which can effectively convert Polish speech into text.

🚀 Quick Start

This model is a fine - tuned version of facebook/wav2vec2-xls-r-1b on Polish. It uses the train and validation splits of Common Voice 8.0, Multilingual LibriSpeech, and Voxpopuli. When using this model, ensure that your speech input is sampled at 16kHz.

This model has been fine - tuned by the HuggingSound tool, and thanks to the GPU credits generously given by the OVHcloud.

✨ Features

• Automatic Speech Recognition: Capable of accurately transcribing Polish speech.
• Fine - tuned on Multiple Datasets: Utilizes data from Common Voice 8.0, Multilingual LibriSpeech, and Voxpopuli for better performance.

💻 Usage Examples

Basic Usage

Using the HuggingSound library:

from huggingsound import SpeechRecognitionModel

model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-xls-r-1b-polish")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]

transcriptions = model.transcribe(audio_paths)

Advanced Usage

Writing your own inference script:

import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

LANG_ID = "pl"
MODEL_ID = "jonatasgrosman/wav2vec2-xls-r-1b-polish"
SAMPLES = 10

test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")

processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
    batch["speech"] = speech_array
    batch["sentence"] = batch["sentence"].upper()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentences = processor.batch_decode(predicted_ids)

🔧 Technical Details

Evaluation Results

📦 Installation

No specific installation steps are provided in the original document.

📚 Documentation

Evaluation Commands

1. To evaluate on mozilla-foundation/common_voice_8_0 with split test

python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-polish --dataset mozilla-foundation/common_voice_8_0 --config pl --split test

2. To evaluate on speech-recognition-community-v2/dev_data

python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-polish --dataset speech-recognition-community-v2/dev_data --config pl --split validation --chunk_length_s 5.0 --stride_length_s 1.0

📄 License

This model is licensed under the Apache - 2.0 license.

📖 Citation

If you want to cite this model you can use this:

@misc{grosman2021xlsr-1b-polish,
  title={Fine-tuned {XLS-R} 1{B} model for speech recognition in {P}olish},
  author={Grosman, Jonatas},
  howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-polish}},
  year={2022}
}