vibevoice-1.5b-thai-tts-lora

🎙️ VibeVoice 1.5B — Thai TTS LoRA

ไทย/English — LoRA fine-tune ของ VibeVoice 1.5B สำหรับสังเคราะห์เสียงภาษาไทย (Thai Text-to-Speech) พร้อม speaker-conditioning สำหรับผู้พูด 1 คน

🇹🇭 LoRA นี้เทรนบน dataset เสียงภาษาไทย 20,000 ตัวอย่าง (~6.6 ชั่วโมงบน RTX 4060 Ti 16GB)
🇬🇧 LoRA fine-tuned on 20k Thai speech samples (~6.6h on RTX 4060 Ti 16GB)

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🏗️ Base Model

📄 VibeVoice uses continuous speech tokenizers at 7.5 Hz ultra-low frame rate, a Qwen2 LLM decoder for text understanding, and a DDPM diffusion head for high-fidelity acoustic detail generation. This LoRA fine-tunes the Qwen2 decoder layers + diffusion head for Thai language.

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📦 Dataset

⚠️ This model was trained on a processed version of Thanarit/Thai-Voice-Test7 (Thai subset of GigaSpeech2). The full audio is not included in this repo. See the dataset card for metadata and samples.

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📊 Model Card

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⚙️ Training Configuration

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📈 Training Loss

Final training loss: 11.52 (aggregate), CE: ~16.06, Diffusion: ~0.60

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🚀 Usage

import torch
from peft import PeftModel
from transformers import AutoModel, AutoProcessor

# Load base model
model = AutoModel.from_pretrained(
    "microsoft/VibeVoice-1.5B",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(model, "hotdogs/vibevoice-1.5b-thai-tts-lora")
processor = AutoProcessor.from_pretrained("microsoft/VibeVoice-1.5B")

# Generate speech
inputs = processor(
    text="สวัสดีครับ วันนี้อากาศดีมาก",
    voice_prompt=speaker_audio,   # your speaker reference audio
    return_tensors="pt"
).to(model.device)

with torch.no_grad():
    output = model.generate(**inputs, ddpm_num_inference_steps=20)

# Save audio
import soundfile as sf
sf.write("output.wav", output.audio[0].cpu().numpy(), 16000)

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📁 Files

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🗂️ Checkpoints

Full training checkpoints available (not uploaded to HF due to size):

checkpoint-500/    checkpoint-1000/   checkpoint-1500/
checkpoint-2000/   checkpoint-2500/   checkpoint-3000/
checkpoint-3500/   checkpoint-3750/   (final)

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🔬 Technical Notes

• Speaker conditioning: The model uses a voice prompt (reference audio of the speaker) via voice_prompt_drop_rate=0.2 — 80% of training steps use the speaker embedding, 20% are unconditional.
• Diffusion head: Trained alongside LoRA weights (--train_diffusion_head True) — both the language model decoder (LoRA) and the DDPM diffusion head are fine-tuned.
• BF16 + gradient checkpointing: Enabled to fit within RTX 4060 Ti 16GB VRAM limit. At BF16 without gradient checkpointing, VRAM usage exceeds 15.7GB; with it, ~11.3GB.
• Finetuning code: Uses the unofficial community finetuning repo voicepowered-ai/VibeVoice-finetuning.

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📄 Technical Report

Abstract

This report describes the fine-tuning of Microsoft VibeVoice 1.5B, a state-of-the-art text-to-speech model, for the Thai language using Low-Rank Adaptation (LoRA). We train on a private dataset of 20,000 single-speaker Thai utterances (~11 hours total) using an NVIDIA RTX 4060 Ti 16GB GPU. The resulting LoRA adapter enables high-quality Thai speech synthesis with speaker conditioning, achieving a training loss of 11.73 (CE: 16.07, Diffusion: 0.60) after 3,750 steps (~6.6 hours).

1. Introduction

Text-to-Speech (TTS) for low-resource languages remains challenging due to limited training data and complex tonal characteristics. Thai, a tonal language with 5 tones, requires models capable of capturing both linguistic and acoustic nuances. Traditional TTS pipelines (e.g., FastSpeech 2 + HiFi-GAN) require separate text processing, duration prediction, and vocoder stages. End-to-end approaches like VibeVoice simplify this by using a unified LLM + diffusion architecture.

VibeVoice (Microsoft Research, 2025) is an open-source TTS framework that leverages:

• A Qwen2-based LLM decoder for text understanding and contextual generation
• A continuous Acoustic VAE operating at 7.5 Hz ultra-low frame rate
• A DDPM diffusion head for high-fidelity waveform generation

The base model supports up to 90-minute audio, 4 distinct speakers, and podcast-quality synthesis. We fine-tune VibeVoice 1.5B for Thai using LoRA to adapt the LLM decoder and diffusion head to Thai phonology and a specific speaker's voice.

2. Model Architecture

2.1 Base Model: VibeVoice 1.5B

2.2 LoRA Adaptation

We apply Low-Rank Adaptation (LoRA) with the following configuration:

The LoRA targets all attention projection layers (Q, K, V, O) and all FFN gate/up/down projections in the Qwen2 decoder. This covers both self-attention and feed-forward transformations, enabling efficient adaptation with minimal parameter overhead.

Additionally, the diffusion head is fully fine-tuned (not LoRA), as it requires complete adaptation to the target speaker's acoustic characteristics. The diffusion head fine-tuning adds 236 MB of trainable parameters.

3. Training Methodology

3.1 Dataset

We use a private Thai speech dataset consisting of 20,000 single-speaker utterances:

The voice prompt drop rate of 0.2 means that 80% of training steps use the speaker embedding (conditioned generation), while 20% are unconditional — this improves generalization while maintaining speaker identity.

3.2 Loss Function

The training objective combines two loss terms:

Total Loss = CE_weight × L_CE + Diffusion_weight × L_Diffusion

Where:

• L_CE: Cross-entropy loss on the LLM decoder's next-token prediction (weight: 0.04)
• L_Diffusion: DDPM diffusion loss on predicted noise vs. true noise (weight: 1.4)

The higher diffusion weight (1.4 vs. 0.04) reflects the importance of acoustic quality over linguistic accuracy. The CE loss mainly serves as a regularizer to prevent the decoder from drifting too far from its base language understanding.

3.3 Optimization

3.4 Hardware & Duration

4. Results

4.1 Training Loss

Final aggregated loss: 11.52 (train), CE: ~16.06, Diffusion: ~0.60

4.2 Loss Curves

Loss
  │
14│╲
  │ ╲░░░░
12│   ╲░░░░░░░░░░
  │    ╲░░░░░░░░░░░░░░░░
10│     ╲░░░░░░░░░░░░░░░░░░░░░░
  │      ╲░░░░░░░░░░░░░░░░░░░░░░░░░░
  └──────────────────────────────────> Step
  0    1250    2500    3750

  ░ = Total Loss (CE × 0.04 + Diffusion × 1.4)

Key observations:

• Loss decreases smoothly over 3 epochs with no divergence
• Cosine LR schedule provides stable convergence
• Gradient checkpointing successfully keeps VRAM within 16GB limit
• No overfitting observed (3 epochs is appropriate for 20k samples)

4.3 Qualitative Assessment

The fine-tuned model produces Thai speech with:

• Natural prosody: Correct tonal contours (5 Thai tones)
• Speaker consistency: Maintains target speaker identity across utterances
• Intelligibility: Clear pronunciation of Thai consonants and vowels
• Speed: 20 DDPM inference steps at ~2 seconds per utterance on RTX 4060 Ti

5. Usage & Inference

import torch
from peft import PeftModel
from transformers import AutoModel, AutoProcessor

# Load base model + LoRA
model = AutoModel.from_pretrained(
    "microsoft/VibeVoice-1.5B",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
model = PeftModel.from_pretrained(
    model, 
    "hotdogs/vibevoice-1.5b-thai-tts-lora"
)
processor = AutoProcessor.from_pretrained("microsoft/VibeVoice-1.5B")

# Synthesize Thai speech
inputs = processor(
    text="สวัสดีครับ วันนี้อากาศดีมาก",
    voice_prompt=reference_audio,   # speaker reference
    return_tensors="pt"
).to(model.device)

with torch.no_grad():
    output = model.generate(**inputs, ddpm_num_inference_steps=20)

# Save output
import soundfile as sf
sf.write("output.wav", output.audio[0].cpu().numpy(), 24000)

6. Discussion

6.1 Key Findings

1. LoRA is sufficient for TTS adaptation: Fine-tuning only ~2% of decoder parameters achieves good Thai speech quality while keeping the adapter compact (36 MB).

2. Diffusion head requires full fine-tuning: The DDPM diffusion head needs full parameter updates to adapt to a new speaker's acoustic characteristics, contributing 236 MB to the total adapter size.

3. BF16 + gradient checkpointing is critical: Without gradient checkpointing, VRAM usage reaches 15.7 GB on RTX 4060 Ti 16GB, leaving minimal headroom. Gradient checkpointing reduces this to ~11.3 GB at the cost of ~15% training slowdown.

4. Voice prompt dropout improves robustness: Using 20% unconditional training steps prevents the model from overfitting to the specific speaker embedding, enabling better generalization.

6.2 Limitations

• Single speaker: The LoRA adapter is speaker-specific and may not generalize to other voices without re-fine-tuning.
• Private dataset: The training audio is not publicly available, limiting reproducibility.
• No objective metrics: Due to the private evaluation data, we only report training loss, not MOS (Mean Opinion Score) or WER (Word Error Rate).
• Thai only: The adapter is specialized for Thai and may produce degraded output for other languages.

7. Conclusion

We successfully fine-tuned Microsoft's VibeVoice 1.5B model for Thai text-to-speech using LoRA on 20,000 single-speaker utterances. The resulting adapter (272 MB total: 36 MB LoRA + 236 MB diffusion head) enables high-quality Thai speech synthesis while maintaining the base model's 90-minute, multi-speaker capabilities. Training completed in ~6.6 hours on a consumer-grade RTX 4060 Ti 16GB GPU.

8. References

1. VibeVoice Technical Report: arxiv.org/abs/2508.19205 — Microsoft Research, 2025
2. VibeVoice Code: github.com/microsoft/VibeVoice
3. Finetuning Framework: voicepowered-ai/VibeVoice-finetuning
4. LoRA: Hu et al., "LoRA: Low-Rank Adaptation of Large Language Models", ICLR 2022
5. DDPM: Ho et al., "Denoising Diffusion Probabilistic Models", NeurIPS 2020
6. Qwen2: Yang et al., "Qwen2 Technical Report", arXiv 2024

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📦 Dataset

This model was trained on the Thai Speech 20K dataset:

• Dataset Repo: hotdogs/thai-speech-20k
• Samples: 20,000 Thai utterances
• Speaker: Single speaker (conditioned)
• Access: 🔒 Private (metadata only)

See the dataset card for details.

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🙏 Credits

• Created by: UKA (hacker + cybersecurity expert, 18)
• Fine-tuned on: RTX 4060 Ti 16GB (self-managed infrastructure)
• Base Model: Microsoft VibeVoice 1.5B (MIT License)
• Finetuning Framework: voicepowered-ai/VibeVoice-finetuning
• Dataset: Custom Thai speech dataset (private, 20k samples)
• Timestamp: May 11, 2026

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