Whisper Small Portuguese Fine-tune (Common Voice 13)
Fine-tuned Whisper Small for Portuguese ASR with 10.3 WER on Common Voice 13.0. Training details, usage, and evaluation.
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Whisper Small Portuguese Fine-tune (Common Voice 13)
TL;DR
- Fine-tuned Whisper Small for Portuguese achieving 10.3 WER on CV13.0 test set
- Trained on Mozilla Common Voice 13.0 Portuguese (8 hrs on 1×A100 80GB)
- Ready to use with 🤗 transformers
pipeline("automatic-speech-recognition")
What This Model Does
This is a Portuguese-specialized version of OpenAI's Whisper Small (244M parameters). The base Whisper Small was trained on 680k hours of multilingual data with weak supervision. This fine-tune narrows the model's focus to Portuguese using the Mozilla Common Voice 13.0 dataset, yielding a measurable Word Error Rate improvement for Portuguese transcription.
The model accepts 16kHz audio and outputs Portuguese text. It's packaged as a standard 🤗 transformers model with safetensors weights, compatible with the automatic-speech-recognition pipeline.
Training Details
Parameter | Value
|---|---
Base model | openai/whisper-small Dataset | mozilla-foundation/common_voice_13_0 (pt config) Train split | train Eval split | test Learning rate | 1e-05 Train batch size | 64 Eval batch size | 32 Optimizer | Adam (β=(0.9,0.999), ε=1e-08) LR schedule | Linear with 500 warmup steps Training steps | 5,000 Hardware | 1× NVIDIA A100 80GB Training time | ~8 hours WER (test) | 10.30 Training followed the Hugging Face Whisper fine-tuning event recipe.
How to Use
from transformers import pipeline
asr = pipeline(
"automatic-speech-recognition",
model="artificialguybr/whisper-small-pt-cv13",
device=0 # or "cuda"
)
result = asr("path/to/portuguese_audio.wav")
print(result["text"])For longer audio, use chunked inference:
result = asr("long_audio.wav", chunk_length_s=30, stride_length_s=5)The model expects 16kHz mono audio. Resample if your source differs.
Recommended Settings
- Language: Portuguese (forced via
generate_kwargs={"language": "portuguese"}) - Task: transcribe (not translate)
- Audio: 16kHz, clear speech, minimal background noise
- Chunk length: 30s for long-form with 5s stride
Limitations
- Optimized for Portuguese only; other languages will degrade
- Performance drops with low-quality audio, heavy accents, or noisy backgrounds
- Not suitable for real-time streaming without additional optimization
- WER of 10.3 means ~1 error per 10 words — review critical transcriptions
License
Inherits from base Whisper Small (MIT) and Common Voice 13.0 (CC0). Check the model card for any additional restrictions.
Common Errors and Fixes
Error | Cause | Fix
|---|---|---
Model not found | Wrong repo ID | Use artificialguybr/whisper-small-pt-cv13 exactly Poor accuracy on non-PT audio | Model is PT-specialized | Use base Whisper for multilingual OOM on GPU | Batch size too large | Reduce batch_size or use CPU offload Slow inference | No chunking on long audio | Add chunk_length_s=30 to pipeline call
FAQ
Q: How does this compare to Whisper Large-v3 for Portuguese? A: Whisper Large-v3 will generally be more accurate but 3× larger and slower. This fine-tune targets a size/accuracy sweet spot for Portuguese-only workloads.
Q: Can I fine-tune this further on my domain data? A: Yes. Use this as a starting checkpoint with a lower learning rate (e.g., 5e-06) and your domain audio.
Q: Does it support timestamps? A: Yes, the transformers pipeline returns chunk-level timestamps when return_timestamps=True.
Sources
- Model card: https://huggingface.co/artificialguybr/whisper-small-pt-cv13
- Base model: https://huggingface.co/openai/whisper-small
- Dataset: https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0
- Training recipe: Hugging Face Whisper fine-tuning event