Wav2vec2 Large Xlsr 53 Spanish

🚀 Fine-tuned XLSR-53 large model for speech recognition in Spanish

This is a fine - tuned model based on facebook/wav2vec2-large-xlsr-53 for Spanish speech recognition, which uses the train and validation splits of Common Voice 6.1.

🚀 Quick Start

This model is fine - tuned on Spanish using the train and validation splits of Common Voice 6.1. When using this model, ensure that your speech input is sampled at 16kHz.

This model has been fine - tuned thanks to the GPU credits generously given by the OVHcloud :)

The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint

✨ Features

• Datasets: Utilizes common_voice and mozilla - foundation/common_voice_6_0 for training.
• Metrics: Evaluated using wer (Word Error Rate) and cer (Character Error Rate).
• Tags: Associated with tags like audio, automatic - speech - recognition, etc.

📦 Installation

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

💻 Usage Examples

Basic Usage

Using the HuggingSound library:

from huggingsound import SpeechRecognitionModel

model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-spanish")
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 = "es"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-spanish"
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)

for i, predicted_sentence in enumerate(predicted_sentences):
    print("-" * 100)
    print("Reference:", test_dataset[i]["sentence"])
    print("Prediction:", predicted_sentence)

📚 Documentation

Evaluation

1. To evaluate on mozilla - foundation/common_voice_6_0 with split test

python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-spanish --dataset mozilla-foundation/common_voice_6_0 --config es --split test

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

python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-spanish --dataset speech-recognition-community-v2/dev_data --config es --split validation --chunk_length_s 5.0 --stride_length_s 1.0

📄 License

This model is licensed under the Apache 2.0 license.

🔧 Technical Details

No specific technical details are provided in the original document, so this section is skipped.

📄 Citation

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

@misc{grosman2021xlsr53-large-spanish,
  title={Fine-tuned {XLSR}-53 large model for speech recognition in {S}panish},
  author={Grosman, Jonatas},
  howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish}},
  year={2021}
}