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--- |
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language: pt |
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datasets: |
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- common_voice |
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- mls |
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- cetuc |
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- lapsbm |
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- voxforge |
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- tedx |
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- sid |
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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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- speech |
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- PyTorch |
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license: apache-2.0 |
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--- |
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# cetuc100-xlsr: Wav2vec 2.0 with CETUC Dataset |
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This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz) dataset. This dataset contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female speakers, each pronouncing approximately 1,000 phonetically balanced sentences selected from the [CETEN-Folha](https://www.linguateca.pt/cetenfolha/) corpus. |
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In this notebook the model is tested against other available Brazilian Portuguese datasets. |
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| Dataset | Train | Valid | Test | |
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|--------------------------------|-------:|------:|------:| |
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| CETUC | 94h | -- | 5.4h | |
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| Common Voice | | -- | 9.5h | |
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| LaPS BM | | -- | 0.1h | |
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| MLS | | -- | 3.7h | |
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| Multilingual TEDx (Portuguese) | | -- | 1.8h | |
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| SID | | -- | 1.0h | |
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| VoxForge | | -- | 0.1h | |
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| Total | | -- | 21.6h | |
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#### Summary |
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| | CETUC | CV | LaPS | MLS | SID | TEDx | VF | AVG | |
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|----------------------|---------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------| |
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| cetuc\_100 (demonstration below)| 0.446 | 0.856 | 0.089 | 0.967 | 1.172 | 0.929 | 0.902 | 0.765 | |
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| cetuc\_100 + 4-gram (demonstration below)|0.339 | 0.734 | 0.076 | 0.961 | 1.188 | 1.227 | 0.801 | 0.760 | |
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## Demonstration |
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```python |
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MODEL_NAME = "lgris/cetuc100-xlsr" |
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``` |
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### Imports and dependencies |
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```python |
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%%capture |
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!pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html |
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!pip install datasets |
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!pip install jiwer |
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!pip install transformers |
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!pip install soundfile |
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!pip install pyctcdecode |
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!pip install https://github.com/kpu/kenlm/archive/master.zip |
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``` |
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```python |
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import jiwer |
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import torchaudio |
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from datasets import load_dataset, load_metric |
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from transformers import ( |
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Wav2Vec2ForCTC, |
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Wav2Vec2Processor, |
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) |
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from pyctcdecode import build_ctcdecoder |
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import torch |
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import re |
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import sys |
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``` |
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### Helpers |
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```python |
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chars_to_ignore_regex = '[\,\?\.\!\;\:\"]' # noqa: W605 |
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def map_to_array(batch): |
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speech, _ = torchaudio.load(batch["path"]) |
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batch["speech"] = speech.squeeze(0).numpy() |
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batch["sampling_rate"] = 16_000 |
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batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") |
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batch["target"] = batch["sentence"] |
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return batch |
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``` |
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```python |
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def calc_metrics(truths, hypos): |
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wers = [] |
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mers = [] |
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wils = [] |
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for t, h in zip(truths, hypos): |
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try: |
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wers.append(jiwer.wer(t, h)) |
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mers.append(jiwer.mer(t, h)) |
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wils.append(jiwer.wil(t, h)) |
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except: # Empty string? |
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pass |
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wer = sum(wers)/len(wers) |
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mer = sum(mers)/len(mers) |
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wil = sum(wils)/len(wils) |
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return wer, mer, wil |
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``` |
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```python |
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def load_data(dataset): |
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data_files = {'test': f'{dataset}/test.csv'} |
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dataset = load_dataset('csv', data_files=data_files)["test"] |
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return dataset.map(map_to_array) |
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``` |
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### Model |
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```python |
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class STT: |
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def __init__(self, |
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model_name, |
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device='cuda' if torch.cuda.is_available() else 'cpu', |
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lm=None): |
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self.model_name = model_name |
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self.model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) |
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self.processor = Wav2Vec2Processor.from_pretrained(model_name) |
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self.vocab_dict = self.processor.tokenizer.get_vocab() |
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self.sorted_dict = { |
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k.lower(): v for k, v in sorted(self.vocab_dict.items(), |
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key=lambda item: item[1]) |
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} |
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self.device = device |
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self.lm = lm |
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if self.lm: |
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self.lm_decoder = build_ctcdecoder( |
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list(self.sorted_dict.keys()), |
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self.lm |
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) |
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def batch_predict(self, batch): |
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features = self.processor(batch["speech"], |
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sampling_rate=batch["sampling_rate"][0], |
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padding=True, |
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return_tensors="pt") |
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input_values = features.input_values.to(self.device) |
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attention_mask = features.attention_mask.to(self.device) |
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with torch.no_grad(): |
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logits = self.model(input_values, attention_mask=attention_mask).logits |
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if self.lm: |
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logits = logits.cpu().numpy() |
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batch["predicted"] = [] |
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for sample_logits in logits: |
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batch["predicted"].append(self.lm_decoder.decode(sample_logits)) |
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else: |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["predicted"] = self.processor.batch_decode(pred_ids) |
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return batch |
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``` |
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### Download datasets |
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```python |
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%%capture |
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!gdown --id 1HFECzIizf-bmkQRLiQD0QVqcGtOG5upI |
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!mkdir bp_dataset |
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!unzip bp_dataset -d bp_dataset/ |
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``` |
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### Tests |
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```python |
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stt = STT(MODEL_NAME) |
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``` |
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#### CETUC |
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```python |
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ds = load_data('cetuc_dataset') |
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result = ds.map(stt.batch_predict, batched=True, batch_size=8) |
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wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) |
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print("CETUC WER:", wer) |
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``` |
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CETUC WER: 0.44677581829220825 |
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#### Common Voice |
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```python |
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ds = load_data('commonvoice_dataset') |
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result = ds.map(stt.batch_predict, batched=True, batch_size=8) |
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wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) |
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print("CV WER:", wer) |
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``` |
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CV WER: 0.8561919899139065 |
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#### LaPS |
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```python |
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ds = load_data('lapsbm_dataset') |
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result = ds.map(stt.batch_predict, batched=True, batch_size=8) |
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wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) |
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print("Laps WER:", wer) |
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``` |
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Laps WER: 0.08955808080808081 |
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#### MLS |
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```python |
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ds = load_data('mls_dataset') |
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result = ds.map(stt.batch_predict, batched=True, batch_size=8) |
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wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) |
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print("MLS WER:", wer) |
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``` |
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MLS WER: 0.9670008790979718 |
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#### SID |
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```python |
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ds = load_data('sid_dataset') |
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result = ds.map(stt.batch_predict, batched=True, batch_size=8) |
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wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) |
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print("Sid WER:", wer) |
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``` |
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Sid WER: 1.1723738343632861 |
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#### TEDx |
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```python |
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ds = load_data('tedx_dataset') |
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result = ds.map(stt.batch_predict, batched=True, batch_size=8) |
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wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) |
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print("TEDx WER:", wer) |
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``` |
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TEDx WER: 0.929976436317539 |
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#### VoxForge |
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```python |
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ds = load_data('voxforge_dataset') |
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result = ds.map(stt.batch_predict, batched=True, batch_size=8) |
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wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) |
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print("VoxForge WER:", wer) |
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``` |
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VoxForge WER: 0.9020183982683985 |
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### Tests with LM |
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```python |
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# !find -type f -name "*.wav" -delete |
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!rm -rf ~/.cache |
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!gdown --id 1GJIKseP5ZkTbllQVgOL98R4yYAcIySFP # trained with wikipedia |
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stt = STT(MODEL_NAME, lm='pt-BR-wiki.word.4-gram.arpa') |
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# !gdown --id 1dLFldy7eguPtyJj5OAlI4Emnx0BpFywg # trained with bp |
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# stt = STT(MODEL_NAME, lm='pt-BR.word.4-gram.arpa') |
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``` |
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#### CETUC |
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```python |
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ds = load_data('cetuc_dataset') |
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result = ds.map(stt.batch_predict, batched=True, batch_size=8) |
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wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) |
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print("CETUC WER:", wer) |
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``` |
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CETUC WER: 0.3396346663354827 |
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#### Common Voice |
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```python |
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ds = load_data('commonvoice_dataset') |
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result = ds.map(stt.batch_predict, batched=True, batch_size=8) |
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wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) |
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print("CV WER:", wer) |
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``` |
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CV WER: 0.7341013242719512 |
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#### LaPS |
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```python |
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ds = load_data('lapsbm_dataset') |
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result = ds.map(stt.batch_predict, batched=True, batch_size=8) |
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wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) |
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print("Laps WER:", wer) |
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``` |
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Laps WER: 0.07612373737373737 |
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#### MLS |
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```python |
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ds = load_data('mls_dataset') |
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result = ds.map(stt.batch_predict, batched=True, batch_size=8) |
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wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) |
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print("MLS WER:", wer) |
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``` |
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MLS WER: 0.960908940243212 |
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#### SID |
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```python |
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ds = load_data('sid_dataset') |
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result = ds.map(stt.batch_predict, batched=True, batch_size=8) |
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wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) |
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print("Sid WER:", wer) |
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``` |
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Sid WER: 1.188118540533579 |
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#### TEDx |
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```python |
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ds = load_data('tedx_dataset') |
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result = ds.map(stt.batch_predict, batched=True, batch_size=8) |
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wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) |
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print("TEDx WER:", wer) |
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``` |
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TEDx WER: 1.2271077178339618 |
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#### VoxForge |
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```python |
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ds = load_data('voxforge_dataset') |
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result = ds.map(stt.batch_predict, batched=True, batch_size=8) |
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wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) |
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print("VoxForge WER:", wer) |
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``` |
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VoxForge WER: 0.800196158008658 |
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