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--- |
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language: ky |
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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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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: Kyrgyz XLSR Wav2Vec2 Large 53 by Anton Lozhkov |
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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 ky |
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type: common_voice |
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args: ky |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 31.88 |
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--- |
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# Wav2Vec2-Large-XLSR-53-Kyrgyz |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Kyrgyz using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. |
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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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test_dataset = load_dataset("common_voice", "ky", split="test[:2%]") |
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processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-kyrgyz") |
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model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-kyrgyz") |
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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["speech"][:2], 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["sentence"][:2]) |
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``` |
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## Evaluation |
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The model can be evaluated as follows on the Kyrgyz test data of Common Voice. |
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```python |
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import torch |
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import torchaudio |
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import urllib.request |
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import tarfile |
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import pandas as pd |
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from tqdm.auto import tqdm |
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from datasets import load_metric |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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# Download the raw data instead of using HF datasets to save disk space |
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data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/ky.tar.gz" |
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filestream = urllib.request.urlopen(data_url) |
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data_file = tarfile.open(fileobj=filestream, mode="r|gz") |
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data_file.extractall() |
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wer = load_metric("wer") |
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processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-kyrgyz") |
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model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-kyrgyz") |
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model.to("cuda") |
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cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/ky/test.tsv", sep='\t') |
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clips_path = "cv-corpus-6.1-2020-12-11/ky/clips/" |
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def clean_sentence(sent): |
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sent = sent.lower() |
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# replace non-alpha characters with space |
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sent = "".join(ch if ch.isalpha() else " " for ch in sent) |
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# remove repeated spaces |
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sent = " ".join(sent.split()) |
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return sent |
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targets = [] |
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preds = [] |
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for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]): |
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row["sentence"] = clean_sentence(row["sentence"]) |
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speech_array, sampling_rate = torchaudio.load(clips_path + row["path"]) |
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resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) |
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row["speech"] = resampler(speech_array).squeeze().numpy() |
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inputs = processor(row["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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targets.append(row["sentence"]) |
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preds.append(processor.batch_decode(pred_ids)[0]) |
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print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets))) |
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``` |
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**Test Result**: 31.88 % |
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## Training |
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The Common Voice `train` and `validation` datasets were used for training. |
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