pengzhiliang
139 lines
3.5 KiB
Markdown
139 lines
3.5 KiB
Markdown
# VibeVoice ASR LoRA Fine-tuning
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This directory contains scripts for LoRA (Low-Rank Adaptation) fine-tuning of the VibeVoice ASR model.
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## Requirements
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```bash
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pip install peft accelerate
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```
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## Toy Dataset
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> **Note**: The `toy_dataset/` included in this directory contains **synthetic audio generated by VibeVoice TTS** for demonstration purposes only. It is NOT a production-quality dataset.
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>
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> When using your own data, you should:
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> - Prepare real audio recordings with accurate transcriptions
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> - Adjust hyperparameters (learning rate, epochs, LoRA rank) based on your dataset size and domain
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> - Consider the audio quality and speaker diversity in your data
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## Data Format
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Training data should be organized as pairs of audio files and JSON labels in the same directory:
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```
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toy_dataset/
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├── 0.mp3
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├── 0.json
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├── 1.mp3
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├── 1.json
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└── ...
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```
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### JSON Label Format
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Each JSON file should have the following structure:
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```json
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{
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"audio_duration": 351.73,
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"audio_path": "0.mp3",
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"segments": [
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{
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"speaker": 0,
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"text": "Hey everyone, welcome back...",
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"start": 0.0,
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"end": 38.68
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},
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{
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"speaker": 1,
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"text": "Thanks for having me...",
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"start": 38.75,
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"end": 77.88
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}
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],
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"hotwords": ["Tea Brew", "Aiden Host"] // optional
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}
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```
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## Training
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### Basic Usage
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```bash
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python lora_finetune.py \
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--model_path microsoft/VibeVoice-ASR \
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--data_dir ./toy_dataset \
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--output_dir ./output \
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--num_train_epochs 3 \
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--per_device_train_batch_size 1 \
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--learning_rate 1e-4 \
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--bf16
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```
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### Full Options
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The script uses HuggingFace's `TrainingArguments`, so all standard options are available:
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```bash
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python lora_finetune.py \
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--model_path microsoft/VibeVoice-ASR \
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--data_dir ./toy_dataset \
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--output_dir ./output \
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--lora_r 16 \
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--lora_alpha 32 \
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--lora_dropout 0.05 \
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--num_train_epochs 3 \
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--per_device_train_batch_size 1 \
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--gradient_accumulation_steps 4 \
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--learning_rate 1e-4 \
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--warmup_ratio 0.1 \
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--weight_decay 0.01 \
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--max_grad_norm 1.0 \
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--logging_steps 10 \
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--save_steps 100 \
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--gradient_checkpointing \
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--bf16 \
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--report_to none
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```
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### Key Parameters
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| Parameter | Default | Description |
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|-----------|---------|-------------|
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| `--lora_r` | 16 | LoRA rank (lower = fewer params, higher = more expressive) |
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| `--lora_alpha` | 32 | LoRA scaling factor (typically 2x rank) |
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| `--lora_dropout` | 0.05 | Dropout for LoRA layers |
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| `--per_device_train_batch_size` | 8 | Batch size per device |
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| `--gradient_accumulation_steps` | 1 | Effective batch size = batch_size × grad_accum |
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| `--learning_rate` | 5e-5 | Learning rate (1e-4 to 2e-4 typical for LoRA) |
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| `--gradient_checkpointing` | False | Enable to reduce memory usage |
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| `--use_hotwords` | True | Include hotwords from JSON as context |
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| `--max_audio_length` | None | Skip audio longer than this (seconds) |
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## Inference with Fine-tuned Model
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```bash
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python inference_lora.py \
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--base_model microsoft/VibeVoice-ASR \
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--lora_path ./output \
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--audio_file ./toy_dataset/0.mp3 \
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--context_info "Hotwords: Tea Brew, Aiden Host"
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```
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## Merging LoRA Weights (Optional)
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To merge LoRA weights into the base model for faster inference:
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```python
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from peft import PeftModel
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# Load base model + LoRA
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model = VibeVoiceASRForConditionalGeneration.from_pretrained("microsoft/VibeVoice-ASR", ...)
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model = PeftModel.from_pretrained(model, "./output")
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# Merge and save
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model = model.merge_and_unload()
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model.save_pretrained("./merged_model")
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```
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