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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 Jonatas Grosman
	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: 82.37
	- name: Test CER
	type: cer
	value: 19.03
	---

	# 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 thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :)

	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("jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn")
	audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]

	transcriptions = model.transcribe(audio_paths)
	```

	Writing your own inference script:

	```python
	import torch
	import librosa
	from datasets import load_dataset
	from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

	LANG_ID = "zh-CN"
	MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn"
	SAMPLES = 10

	test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")

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

	# Preprocessing the datasets.
	# We need to read the audio files as arrays
	def speech_file_to_array_fn(batch):
	speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
	batch["speech"] = speech_array
	batch["sentence"] = batch["sentence"].upper()
	return batch

	test_dataset = test_dataset.map(speech_file_to_array_fn)
	inputs = processor(test_dataset["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)
	predicted_sentences = processor.batch_decode(predicted_ids)

	for i, predicted_sentence in enumerate(predicted_sentences):
	print("-" * 100)
	print("Reference:", test_dataset[i]["sentence"])
	print("Prediction:", predicted_sentence)
	```

	\| Reference \| Prediction \|
	\| ------------- \| ------------- \|
	\| 宋朝末年年间定居粉岭围。 \| 宋朝末年年间定居分定为 \|
	\| 渐渐行动不便 \| 建境行动不片 \|
	\| 二十一年去世。 \| 二十一年去世 \|
	\| 他们自称恰哈拉。 \| 他们自称家哈<unk> \|
	\| 局部干涩的例子包括有口干、眼睛干燥、及阴道干燥。 \| 菊物干寺的例子包括有口肝眼睛干照以及阴到干<unk> \|
	\| 嘉靖三十八年，登进士第三甲第二名。 \| 嘉靖三十八年登进士第三甲第二名 \|
	\| 这一名称一直沿用至今。 \| 这一名称一直沿用是心 \|
	\| 同时乔凡尼还得到包税合同和许多明矾矿的经营权。 \| 同时桥凡妮还得到包税合同和许多民繁矿的经营权 \|
	\| 为了惩罚西扎城和塞尔柱的结盟，盟军在抵达后将外城烧毁。 \| 为了曾罚西扎城和塞尔素的节盟盟军在抵达后将外曾烧毁 \|
	\| 河内盛产黄色无鱼鳞的鳍射鱼。 \| 合类生场环色无鱼林的骑射鱼 \|

	## 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

	LANG_ID = "zh-CN"
	MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn"
	DEVICE = "cuda"

	CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
	"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
	"{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
	"、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
	"『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "'", "ʻ", "ˆ"]

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

	wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
	cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py

	chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"

	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(chars_to_ignore_regex, "", batch["sentence"]).upper()
	return batch

	test_dataset = test_dataset.map(speech_file_to_array_fn)

	# 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.upper() for x in result["pred_strings"]]
	references = [x.upper() 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, chunk_size=1000) * 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 2021-05-13). 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 \|
	\| ------------- \| ------------- \| ------------- \|
	\| 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/jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn}},
	year={2021}
	}
	```