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--- |
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language: ne |
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datasets: |
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- OpenSLR |
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- common_voice |
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metrics: |
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- wer |
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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: wav2vec2-xlsr-nepali |
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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: OpenSLR ne |
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type: OpenSLR |
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args: ne |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 05.97 |
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--- |
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# Wav2Vec2-Large-XLSR-53-Nepali |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Nepali using the [Common Voice](https://huggingface.co/datasets/common_voice), and [OpenSLR ne](http://www.openslr.org/43/). |
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When using this model, make sure that your speech input is sampled at 16kHz. |
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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 torchaudio |
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from datasets import load_dataset |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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!wget https://www.openslr.org/resources/43/ne_np_female.zip |
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!unzip ne_np_female.zip |
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!ls ne_np_female |
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colnames=['path','sentence'] |
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df = pd.read_csv('/content/ne_np_female/line_index.tsv',sep='\\t',header=None,names = colnames) |
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df['path'] = '/content/ne_np_female/wavs/'+df['path'] +'.wav' |
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train, test = train_test_split(df, test_size=0.1) |
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test.to_csv('/content/ne_np_female/line_index_test.csv') |
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test_dataset = load_dataset('csv', data_files='/content/ne_np_female/line_index_test.csv',split = 'train') |
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processor = Wav2Vec2Processor.from_pretrained("gagan3012/wav2vec2-xlsr-nepali") |
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model = Wav2Vec2ForCTC.from_pretrained("gagan3012/wav2vec2-xlsr-nepali") |
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resampler = torchaudio.transforms.Resample(48_000, 16_000) |
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# Preprocessing the datasets. |
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# We need to read the aduio files as arrays |
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def speech_file_to_array_fn(batch): |
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\tspeech_array, sampling_rate = torchaudio.load(batch["path"]) |
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\tbatch["speech"] = resampler(speech_array).squeeze().numpy() |
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\treturn batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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\tlogits = 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["sentence"][:2]) |
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``` |
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#### Result |
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Prediction: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ'] |
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Reference: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ'] |
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## Evaluation |
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The model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French |
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```python |
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import torch |
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import torchaudio |
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from datasets import load_dataset, load_metric |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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import re |
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!wget https://www.openslr.org/resources/43/ne_np_female.zip |
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!unzip ne_np_female.zip |
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!ls ne_np_female |
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colnames=['path','sentence'] |
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df = pd.read_csv('/content/ne_np_female/line_index.tsv',sep='\\t',header=None,names = colnames) |
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df['path'] = '/content/ne_np_female/wavs/'+df['path'] +'.wav' |
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train, test = train_test_split(df, test_size=0.1) |
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test.to_csv('/content/ne_np_female/line_index_test.csv') |
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test_dataset = load_dataset('csv', data_files='/content/ne_np_female/line_index_test.csv',split = 'train') |
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wer = load_metric("wer") |
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processor = Wav2Vec2Processor.from_pretrained("gagan3012/wav2vec2-xlsr-nepali") |
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model = Wav2Vec2ForCTC.from_pretrained("gagan3012/wav2vec2-xlsr-nepali") |
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model.to("cuda") |
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chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]' |
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resampler = torchaudio.transforms.Resample(48_000, 16_000) |
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# Preprocessing the datasets. |
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# We need to read the aduio files as arrays |
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def speech_file_to_array_fn(batch): |
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\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() |
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\tspeech_array, sampling_rate = torchaudio.load(batch["path"]) |
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\tbatch["speech"] = resampler(speech_array).squeeze().numpy() |
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\treturn 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 aduio files as arrays |
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def evaluate(batch): |
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\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) |
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\twith torch.no_grad(): |
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\t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits |
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\tpred_ids = torch.argmax(logits, dim=-1) |
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\tbatch["pred_strings"] = processor.batch_decode(pred_ids) |
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\treturn batch |
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result = test_dataset.map(evaluate, batched=True, batch_size=8) |
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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) |
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``` |
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**Test Result**: 05.97 % |
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## Training |
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The script used for training can be found [here](https://colab.research.google.com/drive/1AHnYWXb5cwfMEa2o4O3TSdasAR3iVBFP?usp=sharing) |
