---
title: Vibevoice_1_5_lora
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/Vibevoice_1_5_lora"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/Vibevoice_1_5_lora.md"
repository: AI-ModelScope/Vibevoice_1_5_lora
last_updated: 2025-09-25
parameters: 265.0M
tensor_type:
  - BF16
  - F32
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 141
stars: 0
---

# Vibevoice_1_5_lora

> Vibevoice_1_5_lora - AI-ModelScope 在 ModelScope 开源的模型。VibeVoice 1.5B Single-Speaker Fine-tuning Guide

- **Repository**: AI-ModelScope/Vibevoice_1_5_lora
- **Parameters**: 265.0M
- **Downloads**: 141
- **Stars**: 0
- **Last updated**: 2025-09-25

Source: https://www.modelscope.cn/models/AI-ModelScope/Vibevoice_1_5_lora

---

# VibeVoice 1.5B Single-Speaker Fine-tuning Guide

This folder contains all the files needed to fine-tune VibeVoice 1.5B for a single speaker (Elise voice).

## Key Improvements

1. **Fixed EOS Token Issue**: The modified `data_vibevoice.py` adds proper `<|endoftext|>` token after speech generation to prevent repetition/looping
2. **Single-Speaker Training**: Uses `voice_prompt_drop_rate=1.0` to train without voice prompts
3. **Audio Quality Filter**: Removes training samples with abrupt cutoffs

## Files Included

- `data_vibevoice.py` - CRITICAL: Modified data collator that adds EOS token (replaces src/data_vibevoice.py)
- `prepare_jinsaryko_elise_dataset.py` - Downloads and prepares the Elise dataset
- `detect_audio_cutoffs.py` - Detects audio files with abrupt endings
- `finetune_elise_single_speaker.sh` - Training script for single-speaker model
- `test_fixed_eos_dummy_voice.py` - Test script for inference

## Quick Start

1. **Prepare the dataset**:
   ```bash
   python prepare_jinsaryko_elise_dataset.py
   ```

2. **Detect and remove bad audio** (optional but recommended):
   ```bash
   python detect_audio_cutoffs.py
   # This will create elise_cleaned/ folder with good samples only
   ```

3. **IMPORTANT: Replace the data collator**:
   ```bash
   cp data_vibevoice.py ../src/data_vibevoice.py
   ```

4. **Train the model**:
   ```bash
   ./finetune_elise_single_speaker.sh
   ```

5. **Test the model**:
   ```bash
   python test_fixed_eos_dummy_voice.py
   ```

## Training Configuration

Key settings in `finetune_elise_single_speaker.sh`:
- `voice_prompt_drop_rate 1.0` - Always drops voice prompts (single-speaker mode)
- `learning_rate 2.5e-5` - Conservative learning rate
- `ddpm_batch_mul 2` - Diffusion batch multiplier
- `diffusion_loss_weight 1.4` - Diffusion loss weight
- `ce_loss_weight 0.04` - Cross-entropy loss weight

## How It Works

1. The model learns to associate "Speaker 0:" with Elise's voice
2. No voice samples needed during inference
3. Proper EOS token ensures clean endings without repetition

## Dataset Format

The training data should be JSONL with this format:
```json
{"text": "Speaker 0: Hello, this is a test.", "audio": "/path/to/audio.wav"}
```

Note: The "Speaker 0:" prefix is REQUIRED for all text entries.