metadata

language: es
license: apache-2.0
datasets:
  - common_voice
  - mozilla-foundation/common_voice_6_0
metrics:
  - wer
  - cer
tags:
  - audio
  - automatic-speech-recognition
  - es
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_6_0
  - robust-speech-event
  - speech
  - xlsr-fine-tuning-week
model-index:
  - name: XLSR Wav2Vec2 Spanish by Jonatas Grosman
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice es
          type: common_voice
          args: es
        metrics:
          - name: Test WER
            type: wer
            value: 8.82
          - name: Test CER
            type: cer
            value: 2.58
          - name: Test WER (+LM)
            type: wer
            value: 6.27
          - name: Test CER (+LM)
            type: cer
            value: 2.06
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: es
        metrics:
          - name: Dev WER
            type: wer
            value: 30.19
          - name: Dev CER
            type: cer
            value: 13.56
          - name: Dev WER (+LM)
            type: wer
            value: 24.71
          - name: Dev CER (+LM)
            type: cer
            value: 12.61

Wav2Vec2-Large-XLSR-53-Spanish

Added custom language model to https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Spanish using the Common Voice. When using this model, make sure 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

Usage

The model can be used directly (without a language model) as follows...

Using the ASRecognition library:

from asrecognition import ASREngine

asr = ASREngine("es", model_path="jonatasgrosman/wav2vec2-large-xlsr-53-spanish")

audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
transcriptions = asr.transcribe(audio_paths)

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)

Reference	Prediction
HABITA EN AGUAS POCO PROFUNDAS Y ROCOSAS.	HABITAN AGUAS POCO PROFUNDAS Y ROCOSAS
OPERA PRINCIPALMENTE VUELOS DE CABOTAJE Y REGIONALES DE CARGA.	OPERA PRINCIPALMENTE VUELO DE CARBOTAJES Y REGIONALES DE CARGAN
PARA VISITAR CONTACTAR PRIMERO CON LA DIRECCIÓN.	PARA VISITAR CONTACTAR PRIMERO CON LA DIRECCIÓN
TRES	TRES
REALIZÓ LOS ESTUDIOS PRIMARIOS EN FRANCIA, PARA CONTINUAR LUEGO EN ESPAÑA.	REALIZÓ LOS ESTUDIOS PRIMARIOS EN FRANCIA PARA CONTINUAR LUEGO EN ESPAÑA
EN LOS AÑOS QUE SIGUIERON, ESTE TRABAJO ESPARTA PRODUJO DOCENAS DE BUENOS JUGADORES.	EN LOS AÑOS QUE SIGUIERON ESTE TRABAJO ESPARTA PRODUJO DOCENA DE BUENOS JUGADORES
SE ESTÁ TRATANDO DE RECUPERAR SU CULTIVO EN LAS ISLAS CANARIAS.	SE ESTÓ TRATANDO DE RECUPERAR SU CULTIVO EN LAS ISLAS CANARIAS
SÍ	SÍ
"FUE ""SACADA"" DE LA SERIE EN EL EPISODIO ""LEAD"", EN QUE ALEXANDRA CABOT REGRESÓ."	FUE SACADA DE LA SERIE EN EL EPISODIO LEED EN QUE ALEXANDRA KAOT REGRESÓ
SE UBICAN ESPECÍFICAMENTE EN EL VALLE DE MOKA, EN LA PROVINCIA DE BIOKO SUR.	SE UBICAN ESPECÍFICAMENTE EN EL VALLE DE MOCA EN LA PROVINCIA DE PÍOCOSUR

Evaluation

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

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

Citation

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

@misc{grosman2021wav2vec2-large-xlsr-53-spanish,
  title={XLSR Wav2Vec2 Spanish by Jonatas Grosman},
  author={Grosman, Jonatas},
  publisher={Hugging Face},
  journal={Hugging Face Hub},
  howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish}},
  year={2021}
}