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--- |
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language: zh |
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datasets: |
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- common_voice |
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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 Taiwanese Mandarin(zh-tw) by Voidful |
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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 zh-TW |
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type: common_voice |
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args: zh-TW |
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metrics: |
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- name: Test CER |
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type: cer |
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value: 25.57 |
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--- |
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|
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# Wav2Vec2-Large-XLSR-53-tw-gpt |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on zh-tw using the [Common Voice](https://huggingface.co/datasets/common_voice). |
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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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[Colab trial](https://colab.research.google.com/drive/1e_z5jQHYbO2YKEaUgzb1ww1WwiAyydAj?usp=sharing) |
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|
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``` |
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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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AutoTokenizer, |
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AutoModelWithLMHead |
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) |
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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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model_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
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device = "cuda" |
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processor_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
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|
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chars_to_ignore_regex = r"[¥•"#$%&'()*+,-/:;<=>@[\]^_`{|}~⦅⦆「」、 、〃〈〉《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏﹑﹔·'℃°•·.﹑︰〈〉─《﹖﹣﹂﹁﹔!?。。"#$%&'()*+,﹐-/:;<=>@[\]^_`{|}~⦅⦆「」、、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏..!\"#$%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]" |
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model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) |
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processor = Wav2Vec2Processor.from_pretrained(processor_name) |
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tokenizer = AutoTokenizer.from_pretrained("ckiplab/gpt2-base-chinese") |
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gpt_model = AutoModelWithLMHead.from_pretrained("ckiplab/gpt2-base-chinese").to(device) |
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|
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resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000) |
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|
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def load_file_to_data(file): |
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batch = {} |
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speech, _ = torchaudio.load(file) |
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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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return batch |
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|
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def predict(data): |
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features = processor(data["speech"], sampling_rate=data["sampling_rate"], padding=True, return_tensors="pt") |
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input_values = features.input_values.to(device) |
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attention_mask = features.attention_mask.to(device) |
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with torch.no_grad(): |
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logits = model(input_values, attention_mask=attention_mask).logits |
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|
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decoded_results = [] |
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for logit in logits: |
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pred_ids = torch.argmax(logit, dim=-1) |
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mask = pred_ids.ge(1).unsqueeze(-1).expand(logit.size()) |
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vocab_size = logit.size()[-1] |
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voice_prob = torch.nn.functional.softmax((torch.masked_select(logit, mask).view(-1,vocab_size)),dim=-1) |
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gpt_input = torch.cat((torch.tensor([tokenizer.cls_token_id]).to(device),pred_ids[pred_ids>0]), 0) |
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gpt_prob = torch.nn.functional.softmax(gpt_model(gpt_input).logits, dim=-1)[:voice_prob.size()[0],:] |
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comb_pred_ids = torch.argmax(gpt_prob*voice_prob, dim=-1) |
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decoded_results.append(processor.decode(comb_pred_ids)) |
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return decoded_results |
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``` |
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Predict |
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```python |
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predict(load_file_to_data('voice file path')) |
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``` |
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## Evaluation |
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The model can be evaluated as follows on the zh-tw test data of Common Voice. |
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CER calculation refer to https://huggingface.co/ctl/wav2vec2-large-xlsr-cantonese |
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|
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env setup: |
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``` |
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!mkdir cer |
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!wget -O cer/cer.py https://huggingface.co/ctl/wav2vec2-large-xlsr-cantonese/raw/main/cer.py |
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!pip install jiwer |
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!pip install torchaudio |
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!pip install datasets transformers |
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``` |
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|
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## Evaluation without LM: |
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```python |
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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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import torch |
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import re |
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import sys |
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|
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model_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
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device = "cuda" |
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processor_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
|
|
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chars_to_ignore_regex = r"[¥•"#$%&'()*+,-/:;<=>@[\]^_`{|}~⦅⦆「」、 、〃〈〉《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏﹑﹔·'℃°•·.﹑︰〈〉─《﹖﹣﹂﹁﹔!?。。"#$%&'()*+,﹐-/:;<=>@[\]^_`{|}~⦅⦆「」、、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏..!\"#$%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]" |
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model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) |
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processor = Wav2Vec2Processor.from_pretrained(processor_name) |
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ds = load_dataset("common_voice", 'zh-TW', split="test") |
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resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000) |
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|
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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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ds = ds.map(map_to_array) |
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|
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def map_to_pred(batch): |
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features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") |
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input_values = features.input_values.to(device) |
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attention_mask = features.attention_mask.to(device) |
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with torch.no_grad(): |
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logits = model(input_values, attention_mask=attention_mask).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["predicted"] = processor.batch_decode(pred_ids) |
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batch["target"] = batch["sentence"] |
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return batch |
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|
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result = ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=list(ds.features.keys())) |
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cer = load_metric("./cer") |
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print("CER: {:2f}".format(100 * cer.compute(predictions=result["predicted"], references=result["target"]))) |
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``` |
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|
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`CER: 28.79`. |
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`TIME: 05:23 min` |
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|
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## Evaluation with GPT: |
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```python |
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import torchaudio |
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from datasets import load_dataset, load_metric |
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from transformers import ( |
|
Wav2Vec2ForCTC, |
|
Wav2Vec2Processor, |
|
) |
|
import torch |
|
import re |
|
import sys |
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from transformers import AutoTokenizer, AutoModelWithLMHead |
|
|
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model_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
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device = "cuda" |
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processor_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
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chars_to_ignore_regex = r"[¥•"#$%&'()*+,-/:;<=>@[\]^_`{|}~⦅⦆「」、 、〃〈〉《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏﹑﹔·'℃°•·.﹑︰〈〉─《﹖﹣﹂﹁﹔!?。。"#$%&'()*+,﹐-/:;<=>@[\]^_`{|}~⦅⦆「」、、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏..!\"#$%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]" |
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|
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tokenizer = AutoTokenizer.from_pretrained("ckiplab/gpt2-base-chinese") |
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lm_model = AutoModelWithLMHead.from_pretrained("ckiplab/gpt2-base-chinese").to(device) |
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model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) |
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processor = Wav2Vec2Processor.from_pretrained(processor_name) |
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|
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ds = load_dataset("common_voice", 'zh-TW', data_dir="./cv-corpus-6.1-2020-12-11", split="test") |
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|
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resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000) |
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|
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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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ds = ds.map(map_to_array) |
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|
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def map_to_pred(batch): |
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features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") |
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input_values = features.input_values.to(device) |
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attention_mask = features.attention_mask.to(device) |
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with torch.no_grad(): |
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logits = model(input_values, attention_mask=attention_mask).logits |
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|
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decoded_results = [] |
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for logit in logits: |
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pred_ids = torch.argmax(logit, dim=-1) |
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mask = pred_ids.ge(1).unsqueeze(-1).expand(logit.size()) |
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vocab_size = logit.size()[-1] |
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voice_prob = torch.nn.functional.softmax((torch.masked_select(logit, mask).view(-1,vocab_size)),dim=-1) |
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lm_input = torch.cat((torch.tensor([tokenizer.cls_token_id]).to(device),pred_ids[pred_ids>0]), 0) |
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lm_prob = torch.nn.functional.softmax(lm_model(lm_input).logits, dim=-1)[:voice_prob.size()[0],:] |
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comb_pred_ids = torch.argmax(lm_prob*voice_prob, dim=-1) |
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decoded_results.append(processor.decode(comb_pred_ids)) |
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|
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batch["predicted"] = decoded_results |
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batch["target"] = batch["sentence"] |
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return batch |
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|
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|
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result = ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=list(ds.features.keys())) |
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|
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cer = load_metric("./cer") |
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print("CER: {:2f}".format(100 * cer.compute(predictions=result["predicted"], references=result["target"]))) |
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``` |
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|
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`CER 25.75`. |
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`TIME: 06:04 min` |
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|
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## Evaluation with BERT: |
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```python |
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import torchaudio |
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from datasets import load_dataset, load_metric |
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from transformers import ( |
|
Wav2Vec2ForCTC, |
|
Wav2Vec2Processor, |
|
) |
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import torch |
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import re |
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import sys |
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from transformers import AutoTokenizer, AutoModelForMaskedLM |
|
|
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model_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
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device = "cuda" |
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processor_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
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chars_to_ignore_regex = r"[¥•"#$%&'()*+,-/:;<=>@[\]^_`{|}~⦅⦆「」、 、〃〈〉《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏﹑﹔·'℃°•·.﹑︰〈〉─《﹖﹣﹂﹁﹔!?。。"#$%&'()*+,﹐-/:;<=>@[\]^_`{|}~⦅⦆「」、、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏..!\"#$%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]" |
|
|
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tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese") |
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lm_model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese").to(device) |
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model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) |
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processor = Wav2Vec2Processor.from_pretrained(processor_name) |
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|
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ds = load_dataset("common_voice", 'zh-TW', data_dir="./cv-corpus-6.1-2020-12-11", split="test") |
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|
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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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ds = ds.map(map_to_array) |
|
|
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def map_to_pred(batch): |
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features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") |
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input_values = features.input_values.to(device) |
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attention_mask = features.attention_mask.to(device) |
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with torch.no_grad(): |
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logits = model(input_values, attention_mask=attention_mask).logits |
|
|
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decoded_results = [] |
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for logit in logits: |
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pred_ids = torch.argmax(logit, dim=-1) |
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mask = ~pred_ids.eq(tokenizer.pad_token_id).unsqueeze(-1).expand(logit.size()) |
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vocab_size = logit.size()[-1] |
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voice_prob = torch.nn.functional.softmax((torch.masked_select(logit, mask).view(-1,vocab_size)),dim=-1) |
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lm_input = torch.masked_select(pred_ids, ~pred_ids.eq(tokenizer.pad_token_id)).unsqueeze(0) |
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mask_lm_prob = voice_prob.clone() |
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for i in range(lm_input.shape[-1]): |
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masked_lm_input = lm_input.clone() |
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masked_lm_input[0][i] = torch.tensor(tokenizer.mask_token_id).to('cuda') |
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lm_prob = torch.nn.functional.softmax(lm_model(masked_lm_input).logits, dim=-1).squeeze(0) |
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mask_lm_prob[i] = lm_prob[i] |
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comb_pred_ids = torch.argmax(mask_lm_prob*voice_prob, dim=-1) |
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decoded_results.append(processor.decode(comb_pred_ids)) |
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|
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batch["predicted"] = decoded_results |
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batch["target"] = batch["sentence"] |
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return batch |
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|
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|
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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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|
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cer = load_metric("./cer") |
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print("CER: {:2f}".format(100 * cer.compute(predictions=result["predicted"], references=result["target"]))) |
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``` |
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`CER 25.57`. |
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`TIME: 09:49 min` |
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|
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## Evaluation with T-TA: |
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setup |
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``` |
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!git clone https://github.com/voidful/pytorch-tta.git |
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!mv ./pytorch-tta/tta ./tta |
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!wget https://github.com/voidful/pytorch-tta/releases/download/wiki_zh/wiki_zh.pt |
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``` |
|
|
|
```python |
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import torchaudio |
|
from datasets import load_dataset, load_metric |
|
from transformers import ( |
|
Wav2Vec2ForCTC, |
|
Wav2Vec2Processor, |
|
) |
|
import torch |
|
import re |
|
import sys |
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from tta.modeling_tta import TTALMModel |
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from transformers import AutoTokenizer |
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import torch |
|
|
|
|
|
|
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model_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
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device = "cuda" |
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processor_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
|
chars_to_ignore_regex = r"[¥•"#$%&'()*+,-/:;<=>@[\]^_`{|}~⦅⦆「」、 、〃〈〉《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏﹑﹔·'℃°•·.﹑︰〈〉─《﹖﹣﹂﹁﹔!?。。"#$%&'()*+,﹐-/:;<=>@[\]^_`{|}~⦅⦆「」、、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏..!\"#$%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]" |
|
|
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tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese") |
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lm_model = TTALMModel("bert-base-chinese") |
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tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese") |
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lm_model.load_state_dict(torch.load("./wiki_zh.pt",map_location=torch.device('cuda'))) |
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lm_model.to('cuda') |
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lm_model.eval() |
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model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) |
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processor = Wav2Vec2Processor.from_pretrained(processor_name) |
|
|
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ds = load_dataset("common_voice", 'zh-TW', data_dir="./cv-corpus-6.1-2020-12-11", split="test") |
|
|
|
resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000) |
|
|
|
def map_to_array(batch): |
|
speech, _ = torchaudio.load(batch["path"]) |
|
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy() |
|
batch["sampling_rate"] = resampler.new_freq |
|
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") |
|
return batch |
|
|
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ds = ds.map(map_to_array) |
|
|
|
def map_to_pred(batch): |
|
features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") |
|
input_values = features.input_values.to(device) |
|
attention_mask = features.attention_mask.to(device) |
|
with torch.no_grad(): |
|
logits = model(input_values, attention_mask=attention_mask).logits |
|
|
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decoded_results = [] |
|
for logit in logits: |
|
pred_ids = torch.argmax(logit, dim=-1) |
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mask = ~pred_ids.eq(tokenizer.pad_token_id).unsqueeze(-1).expand(logit.size()) |
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vocab_size = logit.size()[-1] |
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voice_prob = torch.nn.functional.softmax((torch.masked_select(logit, mask).view(-1,vocab_size)),dim=-1) |
|
lm_input = torch.masked_select(pred_ids, ~pred_ids.eq(tokenizer.pad_token_id)).unsqueeze(0) |
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lm_prob = torch.nn.functional.softmax(lm_model.forward(lm_input)[0], dim=-1).squeeze(0) |
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comb_pred_ids = torch.argmax(lm_prob*voice_prob, dim=-1) |
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decoded_results.append(processor.decode(comb_pred_ids)) |
|
|
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batch["predicted"] = decoded_results |
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batch["target"] = batch["sentence"] |
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return batch |
|
|
|
|
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result = ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=list(ds.features.keys())) |
|
|
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cer = load_metric("./cer") |
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print("CER: {:2f}".format(100 * cer.compute(predictions=result["predicted"], references=result["target"]))) |
|
``` |
|
|
|
`CER: 25.77`. |
|
`TIME: 06:01 min` |
|
|