Datasets:
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license: mit
tags:
- tts
- benchmark
- text-to-speech
- french
- english
language:
- fr
- en
---
# TTS Model Benchmarks
Benchmark results for various TTS models on French and English, with audio samples sorted worst-to-best by WER.
## Summary Results (French - 500 SIWIS phrases)
| Model | Samples | WER Mean | WER Median | RTF | Real-time? |
|-------|---------|----------|------------|-----|------------|
| **Qwen3-TTS 1.7B** | 500 | **23.4%** | **14.3%** | 1.300 | No (0/500) |
| **VibeVoice 0.5B (FT)** | 500 | 35.0% | 22.9% | **0.416** | **Yes (500/500)** |
| CeSAMe CSM-1B 4-bit | 150 | 69.6% | 66.7% | 3.246 | No |
## Summary Results (English)
| Model | Samples | WER Mean | WER Median | RTF |
|-------|---------|----------|------------|-----|
| CeSAMe CSM-1B 4-bit | 150 | 8.7% | 0.0% | 2.857 |
| Qwen3-TTS 1.7B | 300 | 12.9% | 0.0% | 1.690 |
## Audio Samples
Each model folder contains a `worst_to_best/` directory with all generated audio files ranked by WER (worst first). File format: `NNN_werXXX_sampleid.wav`
- `vibevoice_french/worst_to_best/` - 500 audio samples
- `qwen3_tts_french/worst_to_best/` - 500 audio samples
## Structure
```
vibevoice_french/
results.csv # Full benchmark results
worst_to_best/ # Audio ranked by WER (001 = worst)
qwen3_tts_french/
results.csv
worst_to_best/
cesame_unsloth_baseline/
results.csv # EN + FR baseline (no audio)
qwen3_tts_english/
results.csv # EN baseline (no audio)
```
## Evaluation Methodology
- **WER**: Word Error Rate via OpenAI Whisper API transcription
- **RTF**: Generation time / audio duration (< 1.0 = real-time capable)
- **Benchmark**: 500 SIWIS French phrases (seed=42, 15 < len < 300, deduplicated)
## Models
- **VibeVoice-Realtime-0.5B** (Microsoft) - Fine-tuned on SIWIS French → [Rcarvalo/vibevoice](https://huggingface.co/Rcarvalo/vibevoice)
- **CeSAMe CSM-1B** (Sesame) - Unsloth 4-bit quantization
- **Qwen3-TTS-12Hz-1.7B** (Alibaba) - Base model
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