---
language:
- hr
license: cc-by-sa-4.0
library_name: transformers
base_model: openai/whisper-large-v3
datasets:
- classla/Mici_Princ
metrics:
- wer
- cer
pipeline_tag: automatic-speech-recognition
widget:
- example_title: example 1
  src: https://huggingface.co/classla/whisper-large-v3-mici-princ/raw/main/MP_13_65.37-74.67.mp3.wav
- example_title: example 2
  src: https://huggingface.co/classla/whisper-large-v3-mici-princ/raw/main/MP_15_201.53-210.02.mp3.wav
- example_title: example 3
  src: https://huggingface.co/classla/whisper-large-v3-mici-princ/raw/main/MP_15_60.527-67.71.mp3.wav
- example_title: example 4
  src: https://huggingface.co/classla/whisper-large-v3-mici-princ/raw/main/MP_15_68.5-72.45.mp3.wav
---

# Model Card for Model ID

This model was finetuned on the [Mići Princ dataset](https://huggingface.co/datasets/classla/Mici_Princ), 
the audiobook of the translation of _Le Petit Prince_ into the Chakavian dialect of Croatian.

## Model Details

### Model Description

The model, already very potent in standard Croatian, was finetuned for 80 epochs with an effective batch size of 16. Performance was inspected every 4 epochs, and the latest checkpoint
is uploaded here. Character error rate has been brought down from 11.54% to 3.95%, while word error rate has been lowered from 35.43% to 16.83%.

- **Developed by:** Nikola Ljubešić, Peter Rupnik, Tea Perinčić
- **Language(s) (NLP):** Croatian (hrv) - Chakavian dialect (ckm)
- **License:** Creative Commons - Share Alike 4.0
- **Finetuned from model:** openai/whisper-large-v3

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** [GitHub](https://github.com/5roop/mici_princ_whisper)
- **Paper:** Coming soon
- **Dataset:** [Mići Princ](https://huggingface.co/datasets/classla/Mici_Princ)

## Example use:

```python
import torch
from datasets import load_dataset
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from transformers.pipelines.pt_utils import KeyDataset

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_id = "classla/whisper-large-v3-mici-princ"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id,
)

model.to(device)
processor = AutoProcessor.from_pretrained(model_id)

ds = load_dataset("classla/Mici_Princ", split="test")
pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    max_new_tokens=128,
    chunk_length_s=30,
    batch_size=16,
    return_timestamps=True,
    device=device,
)

result = pipe(
    KeyDataset(ds, "audio"),
    generate_kwargs={"language": "croatian"},
)

for i in result:
    print(i)

# Output:
# {'text': ' Šesti planet je biv deset put veći. Na njin je bivav niki stari čovik ki je pisav vele knjige.', 'chunks': [{'timestamp': (0.0, 7.18), 'text': ' Šesti planet je biv deset put veći. Na njin je bivav niki stari čovik ki je pisav vele knjige.'}]}
# ...

```



## Training Details

#### Preprocessing

Model was trained on the `normalized_text` attribute of the [Mići Princ dataset](https://huggingface.co/datasets/classla/Mici_Princ). This means
that the data included capital letters and punctuation, except bullet points, newlines, and quotation marks. Special characters, present in
the dialect, but not in standard Croatian, were substituted.

Only the `train` split was used in training.

#### Training Hyperparameters

```
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    learning_rate=1e-5,
    warmup_steps=100,
    max_steps=277 * 80,
    gradient_checkpointing=True,
    predict_with_generate=True,
    generation_max_length=225,
    save_steps=277,
```

## Evaluation

For evaluation, the `test` split of the [Mići Princ dataset](https://huggingface.co/datasets/classla/Mici_Princ) was used. The test split consists of two known speakers, Autor and Mići Princ, and two unknown speakers, Geograf and Dilavac. Important to note is that each speaker uses a different micro-dialect, so the test data is challenging on including two new micro-dialects.

#### Metrics
| speaker | WER vanilla | WER fine-tuned | WER reduction | CER vanilla | CER fine-tuned| CER reduction | 
|---|---|---|---|---|---|---|
| all | 35.43% |  16.83% | 52.50% | 11.54% | 3.95% | 65.77% |
| Autor | 38.96% | 14.29% | 63.32% | 10.24% | 2.93% | 71.39% |
| Geograf | 20.94% | 11.57% | 44.75% | 4.99% | 2.19% | 56.11% |
| Mići Princ | 45.31% | 16.62% | 63.32% | 12.21% | 5.09% | 58.31% |
| Dilavac |  39.60% | 23.70% | 40.15% | 18.55% | 5.27% | 71.59% |

## Citation 

Coming soon.

## Model Card Authors

Peter Rupnik

## Model Card Contact

[https://huggingface.co/5roop](https://huggingface.co/5roop)