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--- |
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language: nl |
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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 Dutch 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 nl |
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type: common_voice |
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args: nl |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 13.42 |
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- name: Test CER |
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type: cer |
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value: 8.63 |
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--- |
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# Wav2Vec2-Large-XLSR-53-Dutch |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Dutch using the [Common Voice](https://huggingface.co/datasets/common_voice) and [CSS10](https://github.com/Kyubyong/css10). |
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When using this model, make sure that your speech input is sampled at 16kHz. |
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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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```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 = "nl" |
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MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-dutch" |
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test_dataset = load_dataset("common_voice", LANG_ID, split="test[:2%]") |
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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[:2]["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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print("Prediction:", processor.batch_decode(predicted_ids)) |
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print("Reference:", test_dataset[:2]["sentence"]) |
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``` |
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## Evaluation |
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The model can be evaluated as follows on the Dutch 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 = "nl" |
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MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-dutch" |
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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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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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batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper() |
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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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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=32) |
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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"], chunk_size=8000))) |
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print("CER: {:2f}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["sentence"], chunk_size=8000))) |
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``` |
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**Test Result**: |
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- WER: 13.42% |
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- CER: 8.63% |
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