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--- |
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language: es |
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license: apache-2.0 |
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datasets: |
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- common_voice |
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- mozilla-foundation/common_voice_6_0 |
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metrics: |
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- wer |
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- cer |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- es |
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- hf-asr-leaderboard |
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- mozilla-foundation/common_voice_6_0 |
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- robust-speech-event |
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- speech |
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- xlsr-fine-tuning-week |
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model-index: |
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- name: XLSR Wav2Vec2 Spanish by Jonatas Grosman |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice es |
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type: common_voice |
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args: es |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 8.82 |
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- name: Test CER |
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type: cer |
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value: 2.58 |
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- name: Test WER (+LM) |
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type: wer |
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value: 6.27 |
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- name: Test CER (+LM) |
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type: cer |
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value: 2.06 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Robust Speech Event - Dev Data |
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type: speech-recognition-community-v2/dev_data |
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args: es |
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metrics: |
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- name: Dev WER |
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type: wer |
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value: 30.19 |
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- name: Dev CER |
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type: cer |
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value: 13.56 |
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- name: Dev WER (+LM) |
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type: wer |
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value: 24.71 |
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- name: Dev CER (+LM) |
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type: cer |
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value: 12.61 |
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--- |
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# Fine-tuned XLSR-53 large model for speech recognition in Spanish |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Spanish using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice). |
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When using this model, make sure that your speech input is sampled at 16kHz. |
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This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) |
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The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint |
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## Usage |
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The model can be used directly (without a language model) as follows... |
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Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: |
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```python |
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from huggingsound import SpeechRecognitionModel |
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model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-spanish") |
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audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] |
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transcriptions = model.transcribe(audio_paths) |
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``` |
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Writing your own inference script: |
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```python |
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import torch |
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import librosa |
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from datasets import load_dataset |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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LANG_ID = "es" |
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MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-spanish" |
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SAMPLES = 10 |
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test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]") |
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processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) |
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model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) |
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# Preprocessing the datasets. |
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# We need to read the audio files as arrays |
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def speech_file_to_array_fn(batch): |
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speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) |
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batch["speech"] = speech_array |
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batch["sentence"] = batch["sentence"].upper() |
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return batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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predicted_sentences = processor.batch_decode(predicted_ids) |
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for i, predicted_sentence in enumerate(predicted_sentences): |
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print("-" * 100) |
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print("Reference:", test_dataset[i]["sentence"]) |
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print("Prediction:", predicted_sentence) |
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``` |
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| Reference | Prediction | |
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| ------------- | ------------- | |
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| HABITA EN AGUAS POCO PROFUNDAS Y ROCOSAS. | HABITAN AGUAS POCO PROFUNDAS Y ROCOSAS | |
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| OPERA PRINCIPALMENTE VUELOS DE CABOTAJE Y REGIONALES DE CARGA. | OPERA PRINCIPALMENTE VUELO DE CARBOTAJES Y REGIONALES DE CARGAN | |
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| PARA VISITAR CONTACTAR PRIMERO CON LA DIRECCIÓN. | PARA VISITAR CONTACTAR PRIMERO CON LA DIRECCIÓN | |
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| TRES | TRES | |
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| REALIZÓ LOS ESTUDIOS PRIMARIOS EN FRANCIA, PARA CONTINUAR LUEGO EN ESPAÑA. | REALIZÓ LOS ESTUDIOS PRIMARIOS EN FRANCIA PARA CONTINUAR LUEGO EN ESPAÑA | |
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| 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 | |
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| SE ESTÁ TRATANDO DE RECUPERAR SU CULTIVO EN LAS ISLAS CANARIAS. | SE ESTÓ TRATANDO DE RECUPERAR SU CULTIVO EN LAS ISLAS CANARIAS | |
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| SÍ | SÍ | |
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| "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 | |
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| 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 | |
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## Evaluation |
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1. To evaluate on `mozilla-foundation/common_voice_6_0` with split `test` |
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```bash |
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python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-spanish --dataset mozilla-foundation/common_voice_6_0 --config es --split test |
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``` |
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2. To evaluate on `speech-recognition-community-v2/dev_data` |
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```bash |
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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 |
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``` |
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## Citation |
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If you want to cite this model you can use this: |
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```bibtex |
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@misc{grosman2021xlsr53-large-spanish, |
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title={Fine-tuned {XLSR}-53 large model for speech recognition in {S}panish}, |
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author={Grosman, Jonatas}, |
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howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish}}, |
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year={2021} |
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} |
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``` |
