499 lines
20 KiB
Markdown
499 lines
20 KiB
Markdown
---
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language: zh-TW
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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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- hf-asr-leaderboard
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- robust-speech-event
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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: 18.36
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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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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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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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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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resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
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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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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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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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env setup:
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```
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!pip install editdistance
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!pip install torchaudio
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!pip install datasets transformers
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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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from transformers import AutoTokenizer, AutoModelWithLMHead
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from datasets import Audio
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from math import log
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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("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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ds = load_dataset("common_voice", 'zh-TW', split="test")
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ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
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def map_to_array(batch):
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audio = batch["audio"]
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batch["speech"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
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batch["sampling_rate"] = audio["sampling_rate"]
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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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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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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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result = ds.map(map_to_pred, batched=True, batch_size=3, remove_columns=list(ds.features.keys()))
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def cer_cal(groundtruth, hypothesis):
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err = 0
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tot = 0
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for p, t in zip(hypothesis, groundtruth):
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err += float(ed.eval(p.lower(), t.lower()))
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tot += len(t)
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return err / tot
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print("CER: {:2f}".format(100 * cer_cal(result["target"],result["predicted"])))
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```
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`CER: 28.70`.
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`TIME: 04:08 min`
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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 (
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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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from transformers import AutoTokenizer, AutoModelWithLMHead
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from datasets import Audio
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from math import log
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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("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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ds = load_dataset("common_voice", 'zh-TW', split="test")
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ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
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def map_to_array(batch):
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audio = batch["audio"]
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batch["speech"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
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batch["sampling_rate"] = audio["sampling_rate"]
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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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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.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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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=3, remove_columns=list(ds.features.keys()))
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def cer_cal(groundtruth, hypothesis):
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err = 0
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tot = 0
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for p, t in zip(hypothesis, groundtruth):
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err += float(ed.eval(p.lower(), t.lower()))
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tot += len(t)
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return err / tot
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print("CER: {:2f}".format(100 * cer_cal(result["target"],result["predicted"])))
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```
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`CER 25.70`.
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`TIME: 06:04 min`
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## Evaluation with GPT + beam search:
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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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from transformers import AutoTokenizer, AutoModelWithLMHead
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from datasets import Audio
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from math import log
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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("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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ds = load_dataset("common_voice", 'zh-TW', split="test")
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ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
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def map_to_array(batch):
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audio = batch["audio"]
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batch["speech"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
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batch["sampling_rate"] = audio["sampling_rate"]
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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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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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sequences = [[[], 1.0]]
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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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while True:
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all_candidates = list()
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exceed = False
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for seq in sequences:
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tokens, score = seq
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gpt_input = torch.tensor([tokenizer.cls_token_id]+tokens).to(device)
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gpt_prob = torch.nn.functional.softmax(lm_model(gpt_input).logits, dim=-1)[:len(gpt_input),:]
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if len(gpt_input) >= len(voice_prob):
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exceed = True
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comb_pred_ids = gpt_prob*voice_prob[:len(gpt_input)]
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v,i = torch.topk(comb_pred_ids,50,dim=-1)
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for tok_id,tok_prob in zip(i.tolist()[-1],v.tolist()[-1]):
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candidate = [tokens + [tok_id], score + -log(tok_prob)]
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all_candidates.append(candidate)
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ordered = sorted(all_candidates, key=lambda tup: tup[1])
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sequences = ordered[:10]
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if exceed:
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break
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decoded_results.append(processor.decode(sequences[0][0]))
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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=3, remove_columns=list(ds.features.keys()))
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def cer_cal(groundtruth, hypothesis):
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err = 0
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tot = 0
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for p, t in zip(hypothesis, groundtruth):
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err += float(ed.eval(p.lower(), t.lower()))
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tot += len(t)
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return err / tot
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print("CER: {:2f}".format(100 * cer_cal(result["target"],result["predicted"])))
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```
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`CER 18.36`.
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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 (
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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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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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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`
|