15.2 kB
--- | |
language: zh | |
datasets: | |
- common_voice | |
metrics: | |
- cer | |
tags: | |
- audio | |
- automatic-speech-recognition | |
- speech | |
- xlsr-fine-tuning-week | |
license: apache-2.0 | |
model-index: | |
- name: XLSR Wav2Vec2 Large 53 - Chinese (zh-CN), by Yih-Dar SHIEH | |
results: | |
- task: | |
name: Speech Recognition | |
type: automatic-speech-recognition | |
dataset: | |
name: Common Voice zh-CN | |
type: common_voice | |
args: zh-CN | |
metrics: | |
- name: Test CER | |
type: cer | |
value: 20.90 | |
--- | |
# Wav2Vec2-Large-XLSR-53-Chinese-zh-cn-gpt | |
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Chinese (zh-CN) using the [Common Voice](https://huggingface.co/datasets/common_voice), included [Common Voice](https://huggingface.co/datasets/common_voice) Chinese (zh-TW) dataset (converting the label text to simplified Chinese). | |
When using this model, make sure that your speech input is sampled at 16kHz. | |
## Usage | |
The model can be used directly (without a language model) as follows: | |
```python | |
import torch | |
import torchaudio | |
from datasets import load_dataset | |
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | |
test_dataset = load_dataset("common_voice", "zh-CN", split="test") | |
processor = Wav2Vec2Processor.from_pretrained("ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt") | |
model = Wav2Vec2ForCTC.from_pretrained("ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt") | |
resampler = torchaudio.transforms.Resample(48_000, 16_000) | |
# Preprocessing the datasets. | |
# We need to read the aduio files as arrays | |
def speech_file_to_array_fn(batch): | |
speech_array, sampling_rate = torchaudio.load(batch["path"]) | |
batch["speech"] = resampler(speech_array).squeeze().numpy() | |
return batch | |
test_dataset = test_dataset.map(speech_file_to_array_fn) | |
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) | |
with torch.no_grad(): | |
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits | |
predicted_ids = torch.argmax(logits, dim=-1) | |
print("Prediction:", processor.batch_decode(predicted_ids)) | |
print("Reference:", test_dataset[:2]["sentence"]) | |
``` | |
## Evaluation | |
The model can be evaluated as follows on the zh-CN test data of Common Voice. | |
Original CER calculation refer to https://huggingface.co/ctl/wav2vec2-large-xlsr-cantonese | |
```python | |
#!pip install datasets==1.4.1 | |
#!pip install transformers==4.4.0 | |
#!pip install torchaudio | |
#!pip install jiwer | |
import torch | |
import torchaudio | |
from datasets import load_dataset, load_metric | |
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | |
import re | |
import jiwer | |
def chunked_cer(targets, predictions, chunk_size=None): | |
_predictions = [char for seq in predictions for char in list(seq)] | |
_targets = [char for seq in targets for char in list(seq)] | |
if chunk_size is None: return jiwer.wer(_targets, _predictions) | |
start = 0 | |
end = chunk_size | |
H, S, D, I = 0, 0, 0, 0 | |
while start < len(targets): | |
_predictions = [char for seq in predictions[start:end] for char in list(seq)] | |
_targets = [char for seq in targets[start:end] for char in list(seq)] | |
chunk_metrics = jiwer.compute_measures(_targets, _predictions) | |
H = H + chunk_metrics["hits"] | |
S = S + chunk_metrics["substitutions"] | |
D = D + chunk_metrics["deletions"] | |
I = I + chunk_metrics["insertions"] | |
start += chunk_size | |
end += chunk_size | |
return float(S + D + I) / float(H + S + D) | |
test_dataset = load_dataset("common_voice", "zh-CN", split="test") | |
processor = Wav2Vec2Processor.from_pretrained("ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt") | |
model = Wav2Vec2ForCTC.from_pretrained("ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt") | |
model.to("cuda") | |
chars_to_ignore_regex = 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+ "\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\']" | |
resampler = torchaudio.transforms.Resample(48_000, 16_000) | |
# Preprocessing the datasets. | |
# We need to read the aduio files as arrays | |
def speech_file_to_array_fn(batch): | |
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") + " " | |
speech_array, sampling_rate = torchaudio.load(batch["path"]) | |
batch["speech"] = resampler(speech_array).squeeze().numpy() | |
return batch | |
test_dataset = test_dataset.map(speech_file_to_array_fn) | |
# Preprocessing the datasets. | |
# We need to read the aduio files as arrays | |
def evaluate(batch): | |
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) | |
with torch.no_grad(): | |
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits | |
pred_ids = torch.argmax(logits, dim=-1) | |
batch["pred_strings"] = processor.batch_decode(pred_ids) | |
return batch | |
result = test_dataset.map(evaluate, batched=True, batch_size=8) | |
print("CER: {:2f}".format(100 * chunked_cer(predictions=result["pred_strings"], targets=result["sentence"], chunk_size=1000))) | |
``` | |
**Test Result**: 20.902244 % | |
## Training | |
The Common Voice zh-CN `train`, `validation` were used for training, as well as Common Voice zh-TW `train`, `validation` and `test` datasets. | |
The script used for training can be found [to be uploaded later](...) |