Training whisper on a completely new language .....

[hangman121]

how can i train whisper on a completely new language ? i have the transcribed data and the audio how can i do it

[SametDulger]

How to Train Whisper on a Completely New Language

1. Prepare Your Dataset
   You already have transcribed audio + text pairs, which is perfect. Make sure:

Audio format: WAV or FLAC, sampled at 16kHz mono is preferred.

Transcript quality: Clean, normalized text (lowercase, no special symbols unless language-specific).

Dataset split: Divide into training, validation, and test sets (e.g., 80%/10%/10%).

2. Choose Your Training Strategy
   You can either:

Fine-tune a pretrained Whisper model: Recommended because Whisper has learned a lot about audio features already.

Train a new model from scratch: Much harder and requires huge data & compute.

For a new language, fine-tuning an existing Whisper model (like base or small) is usually the best approach.

3. Set Up Your Environment
   Python 3.8+

PyTorch (with GPU support)

whisper repository from OpenAI: https://github.com/openai/whisper

Hugging Face transformers and datasets libraries (optional but recommended)

4. Data Formatting
   You need your dataset in the form Whisper expects:

Audio inputs (usually log-Mel spectrograms generated on the fly or precomputed)

Text targets tokenized by Whisper’s tokenizer (you can reuse Whisper’s tokenizer, but might need to add new tokens if your language has unique characters)

5. Modify Tokenizer if Needed
   If your new language uses characters or tokens not in Whisper’s tokenizer vocabulary:

Extend the tokenizer vocabulary with your new tokens.

This requires reloading the tokenizer and adding tokens, then fine-tuning the model with the updated tokenizer.

6. Fine-Tuning the Model
   Here’s a high-level overview of the steps to fine-tune:

Load pretrained Whisper model (e.g., openai/whisper-small)

Prepare your dataset loader to return audio tensors and tokenized transcripts

Define training loop or use Hugging Face Trainer

Use appropriate loss function (CrossEntropy on token outputs)

Train with a low learning rate (e.g., 1e-5 to 5e-5)

Validate periodically and check loss and WER (word error rate)

7. Example Frameworks
   Whisper Fine-tuning example by Hugging Face (adapt it for your language)

Community repos like openai/whisper + forks with training scripts

8. Compute Requirements
   A good GPU (or multiple GPUs) is needed (e.g., Nvidia RTX 3090 or better)

Training time depends on data size and model size