wav2vec2-large-chinese-zh-cn / README.md

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	---
	language: zh
	datasets:
	- common_voice
	metrics:
	- wer
	- cer
	tags:
	- audio
	- automatic-speech-recognition
	- speech
	- xlsr-fine-tuning-week
	license: apache-2.0
	model-index:
	- name: XLSR Wav2Vec2 Chinese (zh-CN) by wbbbbb
	results:
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice zh-CN
	type: common_voice
	args: zh-CN
	metrics:
	- name: Test WER
	type: wer
	value: 70.47
	- name: Test CER
	type: cer
	value: 12.30
	---
	# Fine-tuned XLSR-53 large model for speech recognition in Chinese

	Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Chinese using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice), [CSS10](https://github.com/Kyubyong/css10) and [ST-CMDS](http://www.openslr.org/38/).
	When using this model, make sure that your speech input is sampled at 16kHz.

	This model has been fine-tuned on RTX3090 for 50h

	The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint

	## Usage

	The model can be used directly (without a language model) as follows...

	Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:

	```python
	from huggingsound import SpeechRecognitionModel
	model = SpeechRecognitionModel("wbbbbb/wav2vec2-large-chinese-zh-cn")
	audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
	transcriptions = model.transcribe(audio_paths)
	```



	## Evaluation

	The model can be evaluated as follows on the Chinese (zh-CN) test data of Common Voice.

	```python
	import torch
	import re
	import librosa
	from datasets import load_dataset, load_metric
	from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
	import warnings
	import os

	os.environ["KMP_AFFINITY"] = ""


	LANG_ID = "zh-CN"
	MODEL_ID = "zh-CN-output-aishell"
	DEVICE = "cuda"

	test_dataset = load_dataset("common_voice", LANG_ID, split="test")

	wer = load_metric("wer")
	cer = load_metric("cer")



	processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
	model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
	model.to(DEVICE)

	# Preprocessing the datasets.
	# We need to read the audio files as arrays
	def speech_file_to_array_fn(batch):
	with warnings.catch_warnings():
	warnings.simplefilter("ignore")
	speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
	batch["speech"] = speech_array
	batch["sentence"] = (
	re.sub("([^\u4e00-\u9fa5\u0030-\u0039])", "", batch["sentence"]).lower() + " "
	)
	return batch


	test_dataset = test_dataset.map(
	speech_file_to_array_fn,
	num_proc=15,
	remove_columns=['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'],
	)

	# Preprocessing the datasets.
	# We need to read the audio 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(DEVICE),
	attention_mask=inputs.attention_mask.to(DEVICE),
	).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)

	predictions = [x.lower() for x in result["pred_strings"]]
	references = [x.lower() for x in result["sentence"]]

	print(
	f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}"
	)
	print(f"CER: {cer.compute(predictions=predictions, references=references) * 100}")

	```

	Test Result:

	In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2022-07-18). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used.

	\| Model \| WER \| CER \|
	\| ------------- \| ------------- \| ------------- \|
	\| wbbbbb/wav2vec2-large-chinese-zh-cn \| 70.47% \| 12.30% \|
	\| jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn \| 82.37% \| 19.03% \|
	\| ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt \| 84.01% \| 20.95% \|


	## Citation
	If you want to cite this model you can use this:

	```bibtex
	@misc{grosman2021xlsr53-large-chinese,
	title={Fine-tuned {XLSR}-53 large model for speech recognition in {C}hinese},
	author={Grosman, Jonatas},
	howpublished={\url{https://huggingface.co/wbbbbb/wav2vec2-large-chinese-zh-cn}},
	year={2021}
	}
	```