whisper-small-full-tibetan

Whisper Small - Tibetan Speech Translation

This model is a fine-tuned version of openai/whisper-small for Tibetan speech-to-text translation.

Model Description

• Model: Whisper Small (244M parameters)
• Language: Tibetan (བོད་སྐད།)
• Task: Speech Translation (Tibetan audio → Text transcription)
• Base Model: openai/whisper-small
• Dataset: lilgoose777/merged-tibetan-titung-goose
• Checkpoint: checkpoint-3000

Intended Uses

This model is designed to transcribe Tibetan speech into written text. It can be used for:

• 📝 Transcribing Tibetan audio recordings
• 🎙️ Building Tibetan speech recognition applications
• 📚 Creating subtitles for Tibetan audio/video content
• 🔬 Research in low-resource language ASR
• 📖 Preserving and digitizing Tibetan oral traditions

Training Details

Dataset

The model was trained on the Merged Tibetan Titung Goose dataset, which combines multiple Tibetan audio sources for improved coverage.

• Dataset: lilgoose777/merged-tibetan-titung-goose
• Train/Test Split: 90/10
• Sampling Rate: 16000 Hz

Training Hyperparameters

The model was fine-tuned with the following configuration:

Training Infrastructure

• Framework: HuggingFace Transformers 4.56.2
• Training Framework: Seq2SeqTrainer
• Optimizer: AdamW
• Metric: Word Error Rate (WER)

Results

Lower WER is better. WER (Word Error Rate) measures the percentage of words that are incorrectly transcribed.

Usage

Quick Start with Pipeline

from transformers import pipeline

# Create transcription pipeline
pipe = pipeline(
    "automatic-speech-recognition",
    model="milanakdj/whisper-small-full-tibetan",
    generate_kwargs={"language": "tibetan", "task": "translate"}
)

# Transcribe audio file
result = pipe("path/to/tibetan_audio.wav")
print(result["text"])

Using Processor and Model

from transformers import WhisperProcessor, WhisperForConditionalGeneration
import torch

# Load model and processor
processor = WhisperProcessor.from_pretrained("milanakdj/whisper-small-full-tibetan")
model = WhisperForConditionalGeneration.from_pretrained("milanakdj/whisper-small-full-tibetan")

# Load your audio (16kHz sampling rate)
# audio_array = ... your audio array ...

# Process audio
input_features = processor(
    audio_array,
    sampling_rate=16000,
    return_tensors="pt"
).input_features

# Generate transcription
forced_decoder_ids = processor.get_decoder_prompt_ids(
    language="tibetan",
    task="translate"
)

with torch.no_grad():
    predicted_ids = model.generate(
        input_features,
        forced_decoder_ids=forced_decoder_ids
    )

# Decode
transcription = processor.batch_decode(
    predicted_ids,
    skip_special_tokens=True
)[0]

print(transcription)

Using with Librosa

import librosa
from transformers import WhisperProcessor, WhisperForConditionalGeneration

# Load model
processor = WhisperProcessor.from_pretrained("milanakdj/whisper-small-full-tibetan")
model = WhisperForConditionalGeneration.from_pretrained("milanakdj/whisper-small-full-tibetan")

# Load audio file (automatically resamples to 16kHz)
audio, sr = librosa.load("tibetan_audio.mp3", sr=16000)

# Process and transcribe
input_features = processor(
    audio,
    sampling_rate=16000,
    return_tensors="pt"
).input_features

forced_decoder_ids = processor.get_decoder_prompt_ids(
    language="tibetan",
    task="translate"
)

predicted_ids = model.generate(
    input_features,
    forced_decoder_ids=forced_decoder_ids
)

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

print(transcription)

Batch Processing Multiple Files

import torch
import librosa
from transformers import WhisperProcessor, WhisperForConditionalGeneration

# Load model
processor = WhisperProcessor.from_pretrained("milanakdj/whisper-small-full-tibetan")
model = WhisperForConditionalGeneration.from_pretrained("milanakdj/whisper-small-full-tibetan")

# Set device
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# Process multiple files
audio_files = ["file1.wav", "file2.wav", "file3.wav"]

for audio_file in audio_files:
    # Load audio
    audio, _ = librosa.load(audio_file, sr=16000)
    
    # Process
    input_features = processor(
        audio,
        sampling_rate=16000,
        return_tensors="pt"
    ).input_features.to(device)
    
    # Generate
    forced_decoder_ids = processor.get_decoder_prompt_ids(
        language="tibetan",
        task="translate"
    )
    
    with torch.no_grad():
        predicted_ids = model.generate(
            input_features,
            forced_decoder_ids=forced_decoder_ids
        )
    
    # Decode
    transcription = processor.batch_decode(
        predicted_ids,
        skip_special_tokens=True
    )[0]
    
    print(f"{audio_file}: {transcription}")

Evaluation

To evaluate the model on your own Tibetan audio dataset:

from transformers import WhisperProcessor, WhisperForConditionalGeneration
from datasets import load_dataset
import evaluate

# Load model
processor = WhisperProcessor.from_pretrained("milanakdj/whisper-small-full-tibetan")
model = WhisperForConditionalGeneration.from_pretrained("milanakdj/whisper-small-full-tibetan")

# Load your dataset
dataset = load_dataset("your_tibetan_dataset")

# Initialize WER metric
wer_metric = evaluate.load("wer")

# Process and evaluate
# ... (see full evaluation code in documentation)

Limitations and Considerations

Known Limitations

1. Domain Specificity: The model is trained on specific Tibetan dialects and domains present in the training data
2. Audio Quality: Performance degrades with:
   • Background noise
   • Poor recording quality
   • Multiple speakers
   • Non-standard dialects
3. Low-Resource Language: As Tibetan is a low-resource language, the model may have limited generalization compared to high-resource language models
4. Code-Switching: May struggle with Tibetan-English or Tibetan-Chinese code-switching

Best Practices

• Audio Format: Use 16kHz mono audio for best results
• Clean Audio: Minimize background noise and ensure clear speech
• Standard Dialect: Model performs best on dialects similar to training data
• Audio Length: Optimal performance on audio clips under 30 seconds

Ethical Considerations

Intended Use

This model is intended for:

• ✅ Transcription of Tibetan speech
• ✅ Educational purposes
• ✅ Research in speech recognition
• ✅ Preservation of Tibetan language

Out-of-Scope Use

• ❌ Surveillance or monitoring without consent
• ❌ Generating misleading transcriptions
• ❌ Any use that violates privacy or human rights

Bias and Fairness

• The model's performance may vary across different Tibetan dialects
• Training data may not represent all Tibetan-speaking communities equally
• Users should evaluate the model on their specific use case before deployment

Citation

If you use this model in your research or application, please cite:

@misc{whisper-small-tibetan-2024,
  author = {Milan Akdj},
  title = {Whisper Small - Tibetan Speech Translation},
  year = {2024},
  publisher = {HuggingFace},
  journal = {HuggingFace Model Hub},
  howpublished = {\url{https://huggingface.co/milanakdj/whisper-small-full-tibetan}}
}

Also cite the original Whisper paper:

@article{radford2022whisper,
  title={Robust Speech Recognition via Large-Scale Weak Supervision},
  author={Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
  journal={arXiv preprint arXiv:2212.04356},
  year={2022}
}

Acknowledgements

• Base Model: OpenAI Whisper Small
• Dataset: Merged Tibetan Titung Goose
• Framework: HuggingFace Transformers
• Training: Fine-tuned using HuggingFace Seq2SeqTrainer

Model Card Authors

Milan Akdj

Contact

For questions or issues with this model, please open an issue on the model repository.

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Additional Information

Model Architecture

Whisper Small uses a Transformer encoder-decoder architecture:

• Encoder: Processes audio features
• Decoder: Generates text transcription
• Parameters: ~244M total parameters

Training Environment

• Platform: RunPod GPU Instance
• Monitoring: TensorBoard logging enabled
• Evaluation: WER metric on held-out test set

Version History

• v1.0: Initial release with checkpoint-3000

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Language: Tibetan (བོད་སྐད།)
License: Apache 2.0
Model Size: ~244M parameters