metadata
language:
- pt
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Large Portuguese
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 pt
type: mozilla-foundation/common_voice_11_0
config: pt
split: test
args: pt
metrics:
- name: WER
type: wer
value: 4.816664144852979
- name: CER
type: cer
value: 1.6052355927195898
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. When using this model, make sure that your speech input is sampled at 16kHz.
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
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 + text normalization + removal of samples with numbers | 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 + text normalization + removal of samples with numbers | 3.56 | 6.15 |