readme: ---
  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}
  }
  ```