|
--- |
|
license: mit |
|
language: |
|
- pt |
|
base_model: |
|
- distil-whisper/distil-large-v3 |
|
pipeline_tag: automatic-speech-recognition |
|
tags: |
|
- asr |
|
- pt |
|
- ptbr |
|
- stt |
|
- speech-to-text |
|
- automatic-speech-recognition |
|
--- |
|
# Distil-Whisper-Large-v3 for Brazilian Portuguese |
|
|
|
<!-- Provide a quick summary of what the model is/does. --> |
|
|
|
This model is a fine-tuned version of distil-whisper-large-v3 for automatic speech recognition (ASR) in Brazilian Portuguese. It was trained using the Common Voice 16 dataset in conjunction with a private dataset transcribed using Whisper Large v3. |
|
|
|
### Model Description |
|
|
|
<!-- Provide a longer summary of what this model is. --> |
|
|
|
The model aims to perform automatic speech transcription in Brazilian Portuguese with high accuracy. By combining data from Common Voice 16 with an automatically transcribed private dataset, the model achieved a Word Error Rate (WER) of 8.93% on the validation set of Common Voice 16. |
|
|
|
- **Model type:** Speech recognition model based on distil-whisper-large-v3 |
|
- **Language(s) (NLP):** Brazilian Portuguese (pt-BR) |
|
- **License:** MIT |
|
- **Finetuned from model [optional]:** distil-whisper/distil-large-v3 |
|
|
|
## How to Get Started with the Model |
|
|
|
You can use the model with the Transformers library: |
|
from transformers import WhisperForConditionalGeneration, WhisperProcessor |
|
|
|
```python |
|
from datasets import load_dataset |
|
from transformers import WhisperProcessor, WhisperForConditionalGeneration |
|
|
|
# Load the validation split of the Common Voice dataset for Portuguese |
|
common_voice = load_dataset("mozilla-foundation/common_voice_11_0", "pt", split="validation") |
|
|
|
# Load the pretrained model and processor |
|
processor = WhisperProcessor.from_pretrained("freds0/distil-whisper-large-v3-ptbr") |
|
model = WhisperForConditionalGeneration.from_pretrained("freds0/distil-whisper-large-v3-ptbr") |
|
|
|
# Select a sample from the dataset |
|
sample = common_voice[0] # You can change the index to select a different sample |
|
|
|
# Get the audio array and sampling rate |
|
audio_input = sample["audio"]["array"] |
|
sampling_rate = sample["audio"]["sampling_rate"] |
|
|
|
# Preprocess the audio |
|
input_features = processor(audio_input, sampling_rate=sampling_rate, return_tensors="pt").input_features |
|
|
|
# Generate transcription |
|
predicted_ids = model.generate(input_features) |
|
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) |
|
print("Transcription:", transcription[0]) |
|
``` |
|
|
|
|
|
|
