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--- |
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language: pt |
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datasets: |
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- CORAA |
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metrics: |
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- wer |
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tags: |
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- audio |
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- speech |
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- wav2vec2 |
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- pt |
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- portuguese-speech-corpus |
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- automatic-speech-recognition |
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- hf-asr-leaderboard |
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- speech |
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- PyTorch |
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license: apache-2.0 |
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model-index: |
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- name: Edresson Casanova XLSR Wav2Vec2 Large 53 Portuguese |
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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: CORAA |
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type: CORAA |
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args: pt |
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metrics: |
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- name: Test CORAA WER |
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type: wer |
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value: 25.26 |
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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 7 |
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type: mozilla-foundation/common_voice_7_0 |
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args: pt |
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metrics: |
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- name: Test WER on Common Voice 7 |
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type: wer |
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value: 20.08 |
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--- |
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# Wav2vec 2.0 trained with CORAA Portuguese Dataset |
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This a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following [CORAA dataset](https://github.com/nilc-nlp/CORAA) |
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# Use this model |
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```python |
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from transformers import AutoTokenizer, Wav2Vec2ForCTC |
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tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-xlsr-coraa-portuguese") |
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model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-xlsr-coraa-portuguese") |
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``` |
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# Results |
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For the results check the [CORAA article](https://arxiv.org/abs/2110.15731) |
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# Example test with Common Voice Dataset |
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```python |
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dataset = load_dataset("common_voice", "pt", split="test", data_dir="./cv-corpus-6.1-2020-12-11") |
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resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000) |
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def map_to_array(batch): |
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speech, _ = torchaudio.load(batch["path"]) |
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batch["speech"] = resampler.forward(speech.squeeze(0)).numpy() |
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batch["sampling_rate"] = resampler.new_freq |
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batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") |
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return batch |
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``` |
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```python |
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ds = dataset.map(map_to_array) |
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result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys())) |
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print(wer.compute(predictions=result["predicted"], references=result["target"])) |
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``` |
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