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--- |
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language: |
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- nl |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- hf-asr-leaderboard |
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- mozilla-foundation/common_voice_8_0 |
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- nl |
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- robust-speech-event |
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datasets: |
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- mozilla-foundation/common_voice_8_0 |
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model-index: |
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- name: XLS-R Wav2Vec2 Dutch 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 8 |
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type: mozilla-foundation/common_voice_8_0 |
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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: 10.38 |
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- name: Test CER |
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type: cer |
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value: 3.04 |
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- name: Test WER (+LM) |
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type: wer |
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value: 6.83 |
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- name: Test CER (+LM) |
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type: cer |
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value: 2.31 |
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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: nl |
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metrics: |
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- name: Dev WER |
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type: wer |
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value: 31.12 |
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- name: Dev CER |
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type: cer |
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value: 15.92 |
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- name: Dev WER (+LM) |
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type: wer |
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value: 23.95 |
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- name: Dev CER (+LM) |
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type: cer |
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value: 14.18 |
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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 - Test Data |
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type: speech-recognition-community-v2/eval_data |
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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: 20.41 |
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--- |
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# Fine-tuned XLS-R 1B model for speech recognition in Dutch |
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Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on Dutch using the train and validation splits of [Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), [Multilingual LibriSpeech](https://www.openslr.org/94/), and [Voxpopuli](https://github.com/facebookresearch/voxpopuli). |
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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 by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool, and thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) |
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## Usage |
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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-xls-r-1b-dutch") |
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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 = "nl" |
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MODEL_ID = "jonatasgrosman/wav2vec2-xls-r-1b-dutch" |
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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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``` |
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## Evaluation Commands |
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1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` |
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```bash |
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python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-dutch --dataset mozilla-foundation/common_voice_8_0 --config nl --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-xls-r-1b-dutch --dataset speech-recognition-community-v2/dev_data --config nl --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{grosman2021xlsr-1b-dutch, |
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title={Fine-tuned {XLS-R} 1{B} model for speech recognition in {D}utch}, |
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author={Grosman, Jonatas}, |
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howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-dutch}}, |
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year={2022} |
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} |
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``` |
