# Whisper Large Chinese (Mandarin)

This model is a fine-tuned version of openai/whisper-large-v2 on Chinese (Mandarin) using the train and validation splits of Common Voice 11. Not all validation split data were used during training, I extracted 1k samples from the validation split to be used for evaluation during fine-tuning.

## Usage


from transformers import pipeline

transcriber = pipeline(
"automatic-speech-recognition",
model="jonatasgrosman/whisper-large-zh-cv11"
)

transcriber.model.config.forced_decoder_ids = (
transcriber.tokenizer.get_decoder_prompt_ids(
language="zh",
)
)

transcription = transcriber("path/to/my_audio.wav")


## Evaluation

I've performed the evaluation of the model using the test split of two datasets, the Common Voice 11 (same dataset used for the fine-tuning) and the Fleurs (dataset not seen during the fine-tuning). As Whisper can transcribe casing and punctuation, I've performed the model evaluation in 2 different scenarios, one using the raw text and the other using the normalized text (lowercase + removal of punctuations). Additionally, for the Fleurs dataset, I've evaluated the model in a scenario where there are no transcriptions of numerical values since the way these values are described in this dataset is different from how they are described in the dataset used in fine-tuning (Common Voice), so it is expected that this difference in the way of describing numerical values will affect the performance of the model for this type of transcription in Fleurs.

### Common Voice 11

CER WER
jonatasgrosman/whisper-large-zh-cv11 9.31 55.94
jonatasgrosman/whisper-large-zh-cv11 + text normalization 9.55 55.02
openai/whisper-large-v2 33.33 101.80
openai/whisper-large-v2 + text normalization 29.90 95.91

### Fleurs

CER WER
jonatasgrosman/whisper-large-zh-cv11 15.00 93.45
jonatasgrosman/whisper-large-zh-cv11 + text normalization 11.76 70.63
jonatasgrosman/whisper-large-zh-cv11 + keep only non-numeric samples 10.95 87.91
jonatasgrosman/whisper-large-zh-cv11 + text normalization + keep only non-numeric samples 7.83 62.12
openai/whisper-large-v2 23.49 101.28
openai/whisper-large-v2 + text normalization 17.58 83.22
openai/whisper-large-v2 + keep only non-numeric samples 21.03 101.95
openai/whisper-large-v2 + text normalization + keep only non-numeric samples 15.22 79.28