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--- |
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language: su |
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datasets: |
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- openslr |
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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: XLSR Wav2Vec2 Sundanese by cahya |
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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 High quality TTS data for Sundanese |
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type: OpenSLR |
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args: su |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 6.19 |
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--- |
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# Wav2Vec2-Large-XLSR-Sundanese |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) |
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on the [OpenSLR High quality TTS data for Sundanese](https://openslr.org/44/). |
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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, load_metric, Dataset |
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from datasets.utils.download_manager import DownloadManager |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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from pathlib import Path |
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import pandas as pd |
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def load_dataset_sundanese(): |
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urls = [ |
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"https://www.openslr.org/resources/44/su_id_female.zip", |
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"https://www.openslr.org/resources/44/su_id_male.zip" |
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] |
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dm = DownloadManager() |
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download_dirs = dm.download_and_extract(urls) |
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data_dirs = [ |
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Path(download_dirs[0])/"su_id_female/wavs", |
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Path(download_dirs[1])/"su_id_male/wavs", |
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] |
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filenames = [ |
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Path(download_dirs[0])/"su_id_female/line_index.tsv", |
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Path(download_dirs[1])/"su_id_male/line_index.tsv", |
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] |
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dfs = [] |
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dfs.append(pd.read_csv(filenames[0], sep='\t4?\t', names=["path", "sentence"])) |
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dfs.append(pd.read_csv(filenames[1], sep='\t\t', names=["path", "sentence"])) |
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for i, dir in enumerate(data_dirs): |
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dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1) |
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df = pd.concat(dfs) |
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# df = df.sample(frac=1, random_state=1).reset_index(drop=True) |
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dataset = Dataset.from_pandas(df) |
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dataset = dataset.remove_columns('__index_level_0__') |
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return dataset.train_test_split(test_size=0.1, seed=1) |
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dataset = load_dataset_sundanese() |
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test_dataset = dataset['test'] |
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processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese") |
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model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese") |
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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 audio files as arrays |
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def speech_file_to_array_fn(batch): |
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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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batch["speech"] = resampler(speech_array).squeeze().numpy() |
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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 or using the [notebook](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb). |
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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, Dataset |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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from datasets.utils.download_manager import DownloadManager |
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import re |
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from pathlib import Path |
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import pandas as pd |
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def load_dataset_sundanese(): |
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urls = [ |
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"https://www.openslr.org/resources/44/su_id_female.zip", |
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"https://www.openslr.org/resources/44/su_id_male.zip" |
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] |
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dm = DownloadManager() |
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download_dirs = dm.download_and_extract(urls) |
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data_dirs = [ |
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Path(download_dirs[0])/"su_id_female/wavs", |
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Path(download_dirs[1])/"su_id_male/wavs", |
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] |
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filenames = [ |
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Path(download_dirs[0])/"su_id_female/line_index.tsv", |
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Path(download_dirs[1])/"su_id_male/line_index.tsv", |
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] |
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dfs = [] |
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dfs.append(pd.read_csv(filenames[0], sep='\t4?\t', names=["path", "sentence"])) |
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dfs.append(pd.read_csv(filenames[1], sep='\t\t', names=["path", "sentence"])) |
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for i, dir in enumerate(data_dirs): |
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dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1) |
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df = pd.concat(dfs) |
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# df = df.sample(frac=1, random_state=1).reset_index(drop=True) |
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dataset = Dataset.from_pandas(df) |
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dataset = dataset.remove_columns('__index_level_0__') |
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return dataset.train_test_split(test_size=0.1, seed=1) |
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dataset = load_dataset_sundanese() |
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test_dataset = dataset['test'] |
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wer = load_metric("wer") |
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processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese") |
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model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese") |
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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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batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() |
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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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batch["speech"] = resampler(speech_array).squeeze().numpy() |
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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("cuda"), attention_mask=inputs.attention_mask.to("cuda")).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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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) |
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``` |
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**Test Result**: 6.19 % |
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## Training |
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[OpenSLR High quality TTS data for Sundanese](https://openslr.org/44/) was used for training. |
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The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb) |
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and to [evaluate it](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb) |
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