---
license: cc-by-nc-4.0
language:
- tl
- ceb
- en
base_model:
- openai/whisper-small
pipeline_tag: automatic-speech-recognition
tags:
- cebuano
- filipino
- tagalog
- cebuano-asr
- bisaya
- bisaya-asr
- code-switching
---

# phcodeswitch-ceb-dvo

Whisper `small` fine-tuned for automatic speech recognition (ASR) of **Davao Cebuano**, including English–Cebuano code-switching.

- **Base model:** [openai/whisper-small](https://huggingface.co/openai/whisper-small)
- **Fine-tuned language:** Cebuano (`ceb`), BCP-47 `ceb`
- **Task:** Automatic speech recognition (`transcribe`)
- **Best WER:** 20.86% (test split, step 1200)
- **License:** CC BY-NC 4.0 — non-commercial research / educational use only

## How to use

Install dependencies:

```bash
pip install --upgrade transformers torch librosa
```

### Option 1 – `pipeline` (quickstart)

```python
from transformers import pipeline
import librosa

pipe = pipeline(
    "automatic-speech-recognition",
    model="eemberda/phcodeswitch-ceb-dvo",
    device=0,  # use -1 for CPU
)

audio, sr = librosa.load("sample.wav", sr=16_000, mono=True)
result = pipe(
    audio,
    generate_kwargs={
        "language": "tl",   # Cebuano is not native to Whisper; Tagalog prompt works best
        "task": "transcribe",
        "num_beams": 5,
    },
)
print(result["text"])
```

### Option 2 – manual inference with processor + model

```python
from transformers import WhisperForConditionalGeneration, WhisperProcessor
import librosa
import torch

model_id = "eemberda/phcodeswitch-ceb-dvo"

processor = WhisperProcessor.from_pretrained(model_id)
model = WhisperForConditionalGeneration.from_pretrained(model_id)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device).eval()

audio, sr = librosa.load("sample.wav", sr=16_000, mono=True)
inputs = processor.feature_extractor(
    audio, sampling_rate=16_000, return_tensors="pt"
).input_features.to(device)

forced_decoder_ids = processor.get_decoder_prompt_ids(
    language="tl", task="transcribe"
)

with torch.no_grad():
    predicted_ids = model.generate(
        inputs,
        forced_decoder_ids=forced_decoder_ids,
        num_beams=5,
    )

transcription = processor.batch_decode(
    predicted_ids, skip_special_tokens=True
)[0].strip()
print(transcription)
```

## Language notes

- Whisper has **no native Cebuano language token**. The model is fine-tuned on
  Cebuano audio but uses the **Tagalog (`tl`)** decoder prompt, which Whisper
  treats as the closest supported related language.
- The model also handles English and English–Cebuano code-switched speech.
- Audio is expected at **16 kHz mono** (resampled automatically by `librosa`
  in the examples above).

## Training details

- Base model: `openai/whisper-small`
- Optimizer: AdamW, learning rate `1e-5`, warmup 100 steps
- Batch size 4 with gradient accumulation 4 (effective batch 16)
- Max steps 1800 (best checkpoint at step 1200), early stopping patience 3
- Mixed precision (fp16), gradient checkpointing, beam search (5) decoding

## Limitations

- Trained on a small, community-contributed dataset; coverage of accents and
  vocabulary is limited.
- For non-commercial research and educational use only (CC BY-NC 4.0).
- Contributed speaker data must not be used for voice cloning, impersonation,
  or voice synthesis. See the project repository's compliance documents.