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--- |
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language: es |
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datasets: |
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- common_voice |
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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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- speech |
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- xlsr-fine-tuning-week |
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license: apache-2.0 |
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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: 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: 8.81 |
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- name: Test CER |
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type: cer |
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value: 2.70 |
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--- |
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# Wav2Vec2-Large-XLSR-53-Spanish |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Spanish using the [Common Voice](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 [ASRecognition](https://github.com/jonatasgrosman/asrecognition) library: |
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```python |
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from asrecognition import ASREngine |
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asr = ASREngine("es", model_path="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 = asr.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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The model can be evaluated as follows on the Spanish test data of Common Voice. |
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```python |
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import torch |
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import re |
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import librosa |
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from datasets import load_dataset, load_metric |
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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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DEVICE = "cuda" |
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CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞", |
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"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]", |
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"{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。", |
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"、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽", |
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"『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"] |
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test_dataset = load_dataset("common_voice", LANG_ID, split="test") |
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wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py |
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cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py |
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chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]" |
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processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) |
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model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) |
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model.to(DEVICE) |
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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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with warnings.catch_warnings(): |
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warnings.simplefilter("ignore") |
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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"] = re.sub(chars_to_ignore_regex, "", 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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# Preprocessing the datasets. |
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# We need to read the audio files as arrays |
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def evaluate(batch): |
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inputs = processor(batch["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.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["pred_strings"] = processor.batch_decode(pred_ids) |
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return batch |
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result = test_dataset.map(evaluate, batched=True, batch_size=8) |
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predictions = [x.upper() for x in result["pred_strings"]] |
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references = [x.upper() for x in result["sentence"]] |
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print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}") |
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print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}") |
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``` |
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**Test Result**: |
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In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-04-22). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used. |
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| Model | WER | CER | |
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| ------------- | ------------- | ------------- | |
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| jonatasgrosman/wav2vec2-large-xlsr-53-spanish | **8.81%** | **2.70%** | |
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| pcuenq/wav2vec2-large-xlsr-53-es | 10.55% | 3.20% | |
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| facebook/wav2vec2-large-xlsr-53-spanish | 16.99% | 5.40% | |
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| mrm8488/wav2vec2-large-xlsr-53-spanish | 19.20% | 5.96% | |
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