Whisper Large Portuguese
This model is a fine-tuned version of openai/whisper-large-v2 on Portuguese 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-pt-cv11"
)
transcriber.model.config.forced_decoder_ids = (
transcriber.tokenizer.get_decoder_prompt_ids(
language="pt",
task="transcribe"
)
)
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-pt-cv11 | 2.52 | 9.56 |
jonatasgrosman/whisper-large-pt-cv11 + text normalization | 1.60 | 4.82 |
openai/whisper-large-v2 | 4.32 | 13.92 |
openai/whisper-large-v2 + text normalization | 2.84 | 7.02 |
Fleurs
CER | WER | |
---|---|---|
jonatasgrosman/whisper-large-pt-cv11 | 4.88 | 12.08 |
jonatasgrosman/whisper-large-pt-cv11 + text normalization | 5.46 | 8.57 |
jonatasgrosman/whisper-large-pt-cv11 + keep only non-numeric samples | 2.35 | 9.00 |
jonatasgrosman/whisper-large-pt-cv11 + text normalization + keep only non-numeric samples | 3.36 | 6.05 |
openai/whisper-large-v2 | 3.52 | 10.55 |
openai/whisper-large-v2 + text normalization | 4.19 | 7.04 |
openai/whisper-large-v2 + keep only non-numeric samples | 2.61 | 9.29 |
openai/whisper-large-v2 + text normalization + keep only non-numeric samples | 3.56 | 6.15 |
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Dataset used to train jonatasgrosman/whisper-large-pt-cv11
Evaluation results
- WER on mozilla-foundation/common_voice_11_0 pttest set self-reported4.817
- CER on mozilla-foundation/common_voice_11_0 pttest set self-reported1.605
- WER on google/fleurs pt_brtest set self-reported8.568
- CER on google/fleurs pt_brtest set self-reported5.463