Instructions to use hotdogs/thai-speech-20k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- VibeVoice
How to use hotdogs/thai-speech-20k with VibeVoice:
import torch, soundfile as sf, librosa, numpy as np from vibevoice.processor.vibevoice_processor import VibeVoiceProcessor from vibevoice.modular.modeling_vibevoice_inference import VibeVoiceForConditionalGenerationInference # Load voice sample (should be 24kHz mono) voice, sr = sf.read("path/to/voice_sample.wav") if voice.ndim > 1: voice = voice.mean(axis=1) if sr != 24000: voice = librosa.resample(voice, sr, 24000) processor = VibeVoiceProcessor.from_pretrained("hotdogs/thai-speech-20k") model = VibeVoiceForConditionalGenerationInference.from_pretrained( "hotdogs/thai-speech-20k", torch_dtype=torch.bfloat16 ).to("cuda").eval() model.set_ddpm_inference_steps(5) inputs = processor(text=["Speaker 0: Hello!\nSpeaker 1: Hi there!"], voice_samples=[[voice]], return_tensors="pt") audio = model.generate(**inputs, cfg_scale=1.3, tokenizer=processor.tokenizer).speech_outputs[0] sf.write("output.wav", audio.cpu().numpy().squeeze(), 24000) - Notebooks
- Google Colab
- Kaggle
🎤 Thai Speech 20K — Private Dataset for VibeVoice Fine-tuning
ไทย/English — ชุดข้อมูลเสียงพูดภาษาไทย 20,000 ประโยค สำหรับ fine-tune โมเดล TTS
🇹🇭 Dataset เสียงภาษาไทย 20,000 ตัวอย่าง ผู้พูด 1 คน
🇬🇧 20,000 Thai speech utterances, single speaker
📊 Dataset Card
| Field | Detail |
|---|---|
| Name | Thai Speech 20K |
| Language | 🇹🇭 Thai |
| Samples | 20,000 utterances |
| Total Duration | ~11 hours |
| Speaker | Single speaker (conditioned) |
| Audio Format | WAV, 24 kHz mono |
| Text Format | UTF-8 transcripts (JSONL) |
| Split | Train only (no public eval split) |
| License | CC-BY-4.0 (metadata only; audio is private) |
| Access | 🔒 Private — audio not included in this repo |
📋 Data Format
JSONL with fields:
text — Thai text transcription (UTF-8)
audio — Path to WAV file (24 kHz, mono)
Example:
{"text": "สวัสดีครับ วันนี้อากาศดีมาก", "audio": "audio/sample_000000.wav"}
🎯 Intended Use
This dataset was created specifically for fine-tuning the microsoft/VibeVoice-1.5B model for Thai text-to-speech with speaker conditioning. It is intended for:
- Text-to-Speech (TTS): Fine-tuning TTS models for Thai language
- Speaker Adaptation: Single-speaker voice cloning/personalization
- Low-resource TTS: Thai TTS research with limited data
⚠️ Limitations
- Single Speaker: Only one speaker — may not generalize to multi-speaker scenarios
- Private Audio: Audio files are not publicly available for privacy reasons
- Domain: General conversational Thai only — no domain-specific vocabulary
- No Evaluation Split: All 20k samples used for training; evaluation done separately
📎 Related Models
- hotdogs/vibevoice-1.5b-thai-tts-lora — LoRA adapter trained on this dataset
🙏 Credits
- Collected by: UKA
- Purpose: Fine-tuning VibeVoice for Thai TTS
- Timestamp: 2026
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support