|
--- |
|
language: zh-TW |
|
datasets: |
|
- common_voice |
|
tags: |
|
- audio |
|
- automatic-speech-recognition |
|
- hf-asr-leaderboard |
|
- robust-speech-event |
|
- speech |
|
- xlsr-fine-tuning-week |
|
license: apache-2.0 |
|
model-index: |
|
- name: XLSR Wav2Vec2 Taiwanese Mandarin(zh-tw) by Voidful |
|
results: |
|
- task: |
|
name: Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: Common Voice zh-TW |
|
type: common_voice |
|
args: zh-TW |
|
metrics: |
|
- name: Test CER |
|
type: cer |
|
value: 18.36 |
|
--- |
|
|
|
# Wav2Vec2-Large-XLSR-53-tw-gpt |
|
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). |
|
When using this model, make sure that your speech input is sampled at 16kHz. |
|
|
|
## Usage |
|
[Colab trial](https://colab.research.google.com/drive/1e_z5jQHYbO2YKEaUgzb1ww1WwiAyydAj?usp=sharing) |
|
|
|
``` |
|
import torchaudio |
|
from datasets import load_dataset, load_metric |
|
from transformers import ( |
|
Wav2Vec2ForCTC, |
|
Wav2Vec2Processor, |
|
AutoTokenizer, |
|
AutoModelWithLMHead |
|
) |
|
import torch |
|
import re |
|
import sys |
|
|
|
model_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
|
device = "cuda" |
|
processor_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
|
|
|
chars_to_ignore_regex = r"[¥•＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､ 、〃〈〉《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏﹑﹔·'℃°•·．﹑︰〈〉─《﹖﹣﹂﹁﹔！？｡。＂＃＄％＆＇（）＊＋，﹐－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏.．!\"#$%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]" |
|
|
|
|
|
model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) |
|
processor = Wav2Vec2Processor.from_pretrained(processor_name) |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("ckiplab/gpt2-base-chinese") |
|
gpt_model = AutoModelWithLMHead.from_pretrained("ckiplab/gpt2-base-chinese").to(device) |
|
|
|
resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000) |
|
|
|
def load_file_to_data(file): |
|
batch = {} |
|
speech, _ = torchaudio.load(file) |
|
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy() |
|
batch["sampling_rate"] = resampler.new_freq |
|
return batch |
|
|
|
def predict(data): |
|
features = processor(data["speech"], sampling_rate=data["sampling_rate"], 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 |
|
|
|
decoded_results = [] |
|
for logit in logits: |
|
pred_ids = torch.argmax(logit, dim=-1) |
|
mask = pred_ids.ge(1).unsqueeze(-1).expand(logit.size()) |
|
vocab_size = logit.size()[-1] |
|
voice_prob = torch.nn.functional.softmax((torch.masked_select(logit, mask).view(-1,vocab_size)),dim=-1) |
|
gpt_input = torch.cat((torch.tensor([tokenizer.cls_token_id]).to(device),pred_ids[pred_ids>0]), 0) |
|
gpt_prob = torch.nn.functional.softmax(gpt_model(gpt_input).logits, dim=-1)[:voice_prob.size()[0],:] |
|
comb_pred_ids = torch.argmax(gpt_prob*voice_prob, dim=-1) |
|
decoded_results.append(processor.decode(comb_pred_ids)) |
|
|
|
return decoded_results |
|
``` |
|
|
|
Predict |
|
```python |
|
predict(load_file_to_data('voice file path')) |
|
``` |
|
|
|
## Evaluation |
|
The model can be evaluated as follows on the zh-tw test data of Common Voice. |
|
CER calculation refer to https://huggingface.co/ctl/wav2vec2-large-xlsr-cantonese |
|
|
|
env setup: |
|
``` |
|
!pip install editdistance |
|
!pip install torchaudio |
|
!pip install datasets transformers |
|
``` |
|
|
|
## Evaluation without LM: |
|
```python |
|
import torchaudio |
|
from datasets import load_dataset, load_metric |
|
from transformers import ( |
|
Wav2Vec2ForCTC, |
|
Wav2Vec2Processor, |
|
) |
|
import torch |
|
import re |
|
import sys |
|
from transformers import AutoTokenizer, AutoModelWithLMHead |
|
from datasets import Audio |
|
from math import log |
|
|
|
model_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
|
device = "cuda" |
|
processor_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
|
chars_to_ignore_regex = r"[¥•＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､ 、〃〈〉《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏﹑﹔·'℃°•·．﹑︰〈〉─《﹖﹣﹂﹁﹔！？｡。＂＃＄％＆＇（）＊＋，﹐－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏.．!\"#$%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]" |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("ckiplab/gpt2-base-chinese") |
|
lm_model = AutoModelWithLMHead.from_pretrained("ckiplab/gpt2-base-chinese").to(device) |
|
model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) |
|
processor = Wav2Vec2Processor.from_pretrained(processor_name) |
|
|
|
ds = load_dataset("common_voice", 'zh-TW', split="test") |
|
ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) |
|
def map_to_array(batch): |
|
audio = batch["audio"] |
|
batch["speech"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0] |
|
batch["sampling_rate"] = audio["sampling_rate"] |
|
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") |
|
return batch |
|
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 |
|
pred_ids = torch.argmax(logits, dim=-1) |
|
batch["predicted"] = processor.batch_decode(pred_ids) |
|
batch["target"] = batch["sentence"] |
|
return batch |
|
|
|
|
|
result = ds.map(map_to_pred, batched=True, batch_size=3, remove_columns=list(ds.features.keys())) |
|
|
|
def cer_cal(groundtruth, hypothesis): |
|
err = 0 |
|
tot = 0 |
|
for p, t in zip(hypothesis, groundtruth): |
|
err += float(ed.eval(p.lower(), t.lower())) |
|
tot += len(t) |
|
return err / tot |
|
print("CER: {:2f}".format(100 * cer_cal(result["target"],result["predicted"]))) |
|
``` |
|
|
|
`CER: 28.70`. |
|
`TIME: 04:08 min` |
|
|
|
## Evaluation with GPT: |
|
```python |
|
import torchaudio |
|
from datasets import load_dataset, load_metric |
|
from transformers import ( |
|
Wav2Vec2ForCTC, |
|
Wav2Vec2Processor, |
|
) |
|
import torch |
|
import re |
|
import sys |
|
from transformers import AutoTokenizer, AutoModelWithLMHead |
|
from datasets import Audio |
|
from math import log |
|
|
|
model_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
|
device = "cuda" |
|
processor_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
|
chars_to_ignore_regex = r"[¥•＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､ 、〃〈〉《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏﹑﹔·'℃°•·．﹑︰〈〉─《﹖﹣﹂﹁﹔！？｡。＂＃＄％＆＇（）＊＋，﹐－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏.．!\"#$%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]" |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("ckiplab/gpt2-base-chinese") |
|
lm_model = AutoModelWithLMHead.from_pretrained("ckiplab/gpt2-base-chinese").to(device) |
|
model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) |
|
processor = Wav2Vec2Processor.from_pretrained(processor_name) |
|
|
|
ds = load_dataset("common_voice", 'zh-TW', split="test") |
|
ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) |
|
def map_to_array(batch): |
|
audio = batch["audio"] |
|
batch["speech"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0] |
|
batch["sampling_rate"] = audio["sampling_rate"] |
|
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") |
|
return batch |
|
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 |
|
|
|
decoded_results = [] |
|
for logit in logits: |
|
pred_ids = torch.argmax(logit, dim=-1) |
|
mask = pred_ids.ge(1).unsqueeze(-1).expand(logit.size()) |
|
vocab_size = logit.size()[-1] |
|
voice_prob = torch.nn.functional.softmax((torch.masked_select(logit, mask).view(-1,vocab_size)),dim=-1) |
|
lm_input = torch.cat((torch.tensor([tokenizer.cls_token_id]).to(device),pred_ids[pred_ids>0]), 0) |
|
lm_prob = torch.nn.functional.softmax(lm_model(lm_input).logits, dim=-1)[:voice_prob.size()[0],:] |
|
comb_pred_ids = torch.argmax(lm_prob*voice_prob, dim=-1) |
|
decoded_results.append(processor.decode(comb_pred_ids)) |
|
|
|
batch["predicted"] = decoded_results |
|
batch["target"] = batch["sentence"] |
|
return batch |
|
|
|
|
|
result = ds.map(map_to_pred, batched=True, batch_size=3, remove_columns=list(ds.features.keys())) |
|
|
|
def cer_cal(groundtruth, hypothesis): |
|
err = 0 |
|
tot = 0 |
|
for p, t in zip(hypothesis, groundtruth): |
|
err += float(ed.eval(p.lower(), t.lower())) |
|
tot += len(t) |
|
return err / tot |
|
print("CER: {:2f}".format(100 * cer_cal(result["target"],result["predicted"]))) |
|
``` |
|
|
|
`CER 25.70`. |
|
`TIME: 06:04 min` |
|
|
|
|
|
## Evaluation with GPT + beam search: |
|
```python |
|
import torchaudio |
|
from datasets import load_dataset, load_metric |
|
from transformers import ( |
|
Wav2Vec2ForCTC, |
|
Wav2Vec2Processor, |
|
) |
|
import torch |
|
import re |
|
import sys |
|
from transformers import AutoTokenizer, AutoModelWithLMHead |
|
from datasets import Audio |
|
from math import log |
|
|
|
model_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
|
device = "cuda" |
|
processor_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
|
chars_to_ignore_regex = r"[¥•＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､ 、〃〈〉《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏﹑﹔·'℃°•·．﹑︰〈〉─《﹖﹣﹂﹁﹔！？｡。＂＃＄％＆＇（）＊＋，﹐－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏.．!\"#$%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]" |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("ckiplab/gpt2-base-chinese") |
|
lm_model = AutoModelWithLMHead.from_pretrained("ckiplab/gpt2-base-chinese").to(device) |
|
model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) |
|
processor = Wav2Vec2Processor.from_pretrained(processor_name) |
|
|
|
ds = load_dataset("common_voice", 'zh-TW', split="test") |
|
ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) |
|
def map_to_array(batch): |
|
audio = batch["audio"] |
|
batch["speech"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0] |
|
batch["sampling_rate"] = audio["sampling_rate"] |
|
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") |
|
return batch |
|
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 |
|
|
|
decoded_results = [] |
|
for logit in logits: |
|
sequences = [[[], 1.0]] |
|
pred_ids = torch.argmax(logit, dim=-1) |
|
mask = pred_ids.ge(1).unsqueeze(-1).expand(logit.size()) |
|
vocab_size = logit.size()[-1] |
|
voice_prob = torch.nn.functional.softmax((torch.masked_select(logit, mask).view(-1,vocab_size)),dim=-1) |
|
while True: |
|
all_candidates = list() |
|
exceed = False |
|
for seq in sequences: |
|
tokens, score = seq |
|
gpt_input = torch.tensor([tokenizer.cls_token_id]+tokens).to(device) |
|
gpt_prob = torch.nn.functional.softmax(lm_model(gpt_input).logits, dim=-1)[:len(gpt_input),:] |
|
if len(gpt_input) >= len(voice_prob): |
|
exceed = True |
|
comb_pred_ids = gpt_prob*voice_prob[:len(gpt_input)] |
|
v,i = torch.topk(comb_pred_ids,50,dim=-1) |
|
for tok_id,tok_prob in zip(i.tolist()[-1],v.tolist()[-1]): |
|
candidate = [tokens + [tok_id], score + -log(tok_prob)] |
|
all_candidates.append(candidate) |
|
ordered = sorted(all_candidates, key=lambda tup: tup[1]) |
|
sequences = ordered[:10] |
|
if exceed: |
|
break |
|
decoded_results.append(processor.decode(sequences[0][0])) |
|
|
|
batch["predicted"] = decoded_results |
|
batch["target"] = batch["sentence"] |
|
return batch |
|
|
|
|
|
result = ds.map(map_to_pred, batched=True, batch_size=3, remove_columns=list(ds.features.keys())) |
|
|
|
def cer_cal(groundtruth, hypothesis): |
|
err = 0 |
|
tot = 0 |
|
for p, t in zip(hypothesis, groundtruth): |
|
err += float(ed.eval(p.lower(), t.lower())) |
|
tot += len(t) |
|
return err / tot |
|
print("CER: {:2f}".format(100 * cer_cal(result["target"],result["predicted"]))) |
|
``` |
|
|
|
`CER 18.36`. |
|
|
|
|
|
## Evaluation with BERT: |
|
```python |
|
import torchaudio |
|
from datasets import load_dataset, load_metric |
|
from transformers import ( |
|
Wav2Vec2ForCTC, |
|
Wav2Vec2Processor, |
|
) |
|
import torch |
|
import re |
|
import sys |
|
from transformers import AutoTokenizer, AutoModelForMaskedLM |
|
|
|
model_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
|
device = "cuda" |
|
processor_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
|
chars_to_ignore_regex = r"[¥•＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､ 、〃〈〉《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏﹑﹔·'℃°•·．﹑︰〈〉─《﹖﹣﹂﹁﹔！？｡。＂＃＄％＆＇（）＊＋，﹐－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏.．!\"#$%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]" |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese") |
|
lm_model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese").to(device) |
|
model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) |
|
processor = Wav2Vec2Processor.from_pretrained(processor_name) |
|
|
|
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 |
|
|
|
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 |
|
|
|
decoded_results = [] |
|
for logit in logits: |
|
pred_ids = torch.argmax(logit, dim=-1) |
|
mask = ~pred_ids.eq(tokenizer.pad_token_id).unsqueeze(-1).expand(logit.size()) |
|
vocab_size = logit.size()[-1] |
|
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) |
|
mask_lm_prob = voice_prob.clone() |
|
for i in range(lm_input.shape[-1]): |
|
masked_lm_input = lm_input.clone() |
|
masked_lm_input[0][i] = torch.tensor(tokenizer.mask_token_id).to('cuda') |
|
lm_prob = torch.nn.functional.softmax(lm_model(masked_lm_input).logits, dim=-1).squeeze(0) |
|
mask_lm_prob[i] = lm_prob[i] |
|
comb_pred_ids = torch.argmax(mask_lm_prob*voice_prob, dim=-1) |
|
decoded_results.append(processor.decode(comb_pred_ids)) |
|
|
|
batch["predicted"] = decoded_results |
|
batch["target"] = batch["sentence"] |
|
return batch |
|
|
|
|
|
result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys())) |
|
|
|
def cer_cal(groundtruth, hypothesis): |
|
err = 0 |
|
tot = 0 |
|
for p, t in zip(hypothesis, groundtruth): |
|
err += float(ed.eval(p.lower(), t.lower())) |
|
tot += len(t) |
|
return err / tot |
|
print("CER: {:2f}".format(100 * cer_cal(result["target"],result["predicted"]))) |
|
``` |
|
`CER 25.57`. |
|
`TIME: 09:49 min` |
|
|
|
## Evaluation with T-TA: |
|
setup |
|
``` |
|
!git clone https://github.com/voidful/pytorch-tta.git |
|
!mv ./pytorch-tta/tta ./tta |
|
!wget https://github.com/voidful/pytorch-tta/releases/download/wiki_zh/wiki_zh.pt |
|
``` |
|
|
|
```python |
|
import torchaudio |
|
from datasets import load_dataset, load_metric |
|
from transformers import ( |
|
Wav2Vec2ForCTC, |
|
Wav2Vec2Processor, |
|
) |
|
import torch |
|
import re |
|
import sys |
|
from tta.modeling_tta import TTALMModel |
|
from transformers import AutoTokenizer |
|
import torch |
|
|
|
|
|
|
|
model_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
|
device = "cuda" |
|
processor_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" |
|
chars_to_ignore_regex = r"[¥•＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､ 、〃〈〉《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏﹑﹔·'℃°•·．﹑︰〈〉─《﹖﹣﹂﹁﹔！？｡。＂＃＄％＆＇（）＊＋，﹐－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏.．!\"#$%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]" |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese") |
|
lm_model = TTALMModel("bert-base-chinese") |
|
tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese") |
|
lm_model.load_state_dict(torch.load("./wiki_zh.pt",map_location=torch.device('cuda'))) |
|
lm_model.to('cuda') |
|
lm_model.eval() |
|
model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) |
|
processor = Wav2Vec2Processor.from_pretrained(processor_name) |
|
|
|
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 |
|
|
|
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 |
|
|
|
decoded_results = [] |
|
for logit in logits: |
|
pred_ids = torch.argmax(logit, dim=-1) |
|
mask = ~pred_ids.eq(tokenizer.pad_token_id).unsqueeze(-1).expand(logit.size()) |
|
vocab_size = logit.size()[-1] |
|
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) |
|
lm_prob = torch.nn.functional.softmax(lm_model.forward(lm_input)[0], dim=-1).squeeze(0) |
|
comb_pred_ids = torch.argmax(lm_prob*voice_prob, dim=-1) |
|
decoded_results.append(processor.decode(comb_pred_ids)) |
|
|
|
batch["predicted"] = decoded_results |
|
batch["target"] = batch["sentence"] |
|
return batch |
|
|
|
|
|
result = ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=list(ds.features.keys())) |
|
|
|
def cer_cal(groundtruth, hypothesis): |
|
err = 0 |
|
tot = 0 |
|
for p, t in zip(hypothesis, groundtruth): |
|
err += float(ed.eval(p.lower(), t.lower())) |
|
tot += len(t) |
|
return err / tot |
|
print("CER: {:2f}".format(100 * cer_cal(result["target"],result["predicted"]))) |
|
``` |
|
|
|
`CER: 25.77`. |
|
`TIME: 06:01 min` |
|
|
