--- license: cc-by-nc-nd-4.0 tags: - tts - text-to-speech - zero-shot-tts - evaluation - benchmark - speech - robustness - audio - english pretty_name: Raon-OpenTTS-Eval size_categories: - 1K Raon OpenTTS

A robustness-oriented evaluation benchmark for zero-shot text-to-speech, covering **4 acoustic regimes** (Clean, Noisy, Wild, Expressive) across **12 datasets** with **6,000 prompt–text pairs**. Existing zero-shot TTS benchmarks typically evaluate models using prompts drawn from a single read-speech dataset, providing an incomplete view of robustness under realistic and challenging recording scenarios. Raon-OpenTTS-Eval addresses this by sampling prompts from diverse real-world conditions, enabling systematic analysis of TTS robustness across controlled, noisy, conversational, and expressive speech. ## Dataset Structure ``` Raon-OpenTTS-Eval/ ├── clean/ │ ├── metadata.csv # 2,500 pairs │ └── audio/ # reference (prompt) WAV files ├── noisy/ │ ├── metadata.csv # 1,000 pairs │ └── audio/ ├── wild/ │ ├── metadata.csv # 1,000 pairs │ └── audio/ └── expressive/ ├── metadata.csv # 1,500 pairs └── audio/ ``` Each `metadata.csv` has the following columns: | Column | Description | |--------|-------------| | `category` | Acoustic regime (CLEAN / NOISY / WILD / EXPRESSIVE) | | `source` | Source dataset name | | `ref_id` | Prompt utterance ID | | `ref_dur` | Prompt duration (seconds) | | `ref_text` | Prompt transcription (condition for zero-shot TTS) | | `gen_id` | Target utterance ID (used to name generated wav) | | `gen_dur` | Target duration (seconds) | | `gen_text` | Target text to synthesize | | `ref_audio` | Relative path to prompt WAV (`audio/{filename}.wav`) | ## Construction For each source dataset, 500 utterances are selected as speech prompts via **stratified sampling by speaker metadata** (emotion, dialect, speaking style) to ensure representative coverage. Each prompt is paired with a target text drawn from a **disjoint utterance in the same dataset**, resulting in cross-sentence pairs. For **AMI-SDM**, a substantial number of segments contain noisy or misaligned transcriptions due to distant microphone recording conditions. To ensure reliable evaluation, only segments with **zero WER** (as estimated by Whisper) are retained before sampling, filtering out samples with severe transcription mismatches. ## Quick Start: Evaluation ### 1. Install dependencies ```bash pip install faster-whisper whisper-normalizer jiwer torchaudio soundfile torch ``` ### 2. Download the WavLM speaker verification checkpoint The SIM metric uses an ECAPA-TDNN model with WavLM-large features, finetuned for speaker verification. Download the checkpoint from [UniSpeech](https://github.com/microsoft/UniSpeech/tree/main/downstreams/speaker_verification): ```bash # Direct download wget https://github.com/microsoft/UniSpeech/releases/download/v1.0.0/wavlm_large_finetune.pth ``` > `ecapa_tdnn.py` (included in this repository) must be in the same directory as `eval_raon_tts.py` when running evaluation. ### 3. Generate audio For each row in `metadata.csv`, synthesize `gen_text` conditioned on the prompt audio at `ref_audio`. Save the output as `{gen_id}.wav` in a flat directory. ```python for row in metadata: wav = your_tts_model.synthesize( text=row["gen_text"], prompt_audio=f"{split_dir}/{row['ref_audio']}", prompt_text=row["ref_text"], ) save_wav(wav, f"{output_dir}/{row['gen_id']}.wav") ``` ### 4. Run evaluation ```bash python eval_raon_tts.py \ --gen_dir /path/to/generated_wavs \ --dataset_dir /path/to/Raon-OpenTTS-Eval \ --wavlm_ckpt /path/to/wavlm_large_finetune.pth ``` `--gen_dir` accepts two layouts: | Layout | Expected structure | |--------|-------------------| | **Flat** | `gen_dir/{gen_id}.wav` | | **Per-split** | `gen_dir/{split}/wavs/{gen_id}.wav` | Split names recognized: `clean` / `raon-clean`, `noisy` / `raon-noisy`, `wild` / `raon-wild`, `expressive` / `raon-emo`. ### 5. Output ``` RESULTS SUMMARY ================================================== clean WER=0.0199 SIM=0.6793 noisy WER=0.0341 SIM=0.6969 wild WER=0.0641 SIM=0.6017 expressive WER=0.0117 SIM=0.6020 overall WER=0.0300 SIM=0.6505 ================================================== Results saved to: /path/to/generated_wavs/raon_eval_results.json ``` **Metrics:** - **WER** — Word Error Rate computed by transcribing generated audio with Whisper-large-v3 and normalizing via `EnglishTextNormalizer` (avoids penalizing surface-form variants such as numeric expressions or hyphenated compounds) - **SIM** — Cosine speaker similarity between generated and prompt audio using WavLM-large finetuned for speaker verification ## Baseline Results Zero-shot TTS models evaluated under the protocol above. WER (%) via Whisper-large-v3 (normalized); SIM via WavLM-large. Overall is computed over all evaluation samples across the four categories. Bold marks the best result and the Raon-OpenTTS rows. Numbers are from the [technical report](https://arxiv.org/abs/2605.20830). | Model | Clean WER ↓ | Clean SIM ↑ | Noisy WER ↓ | Noisy SIM ↑ | Wild WER ↓ | Wild SIM ↑ | Expressive WER ↓ | Expressive SIM ↑ | Overall WER ↓ | Overall SIM ↑ | |---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| | F5-TTS | 2.17 | 0.613 | 3.82 | 0.640 | 136.03 | 0.324 | 3.46 | 0.503 | 25.08 | 0.542 | | MaskGCT | 3.39 | 0.672 | 5.56 | 0.727 | 28.00 | 0.581 | 6.44 | 0.546 | 8.61 | 0.635 | | CosyVoice 2 | 2.59 | 0.642 | 4.39 | 0.675 | 49.73 | 0.535 | 3.66 | 0.536 | 11.02 | 0.603 | | CosyVoice 3 | 2.53 | 0.678 | 3.69 | 0.720 | 8.31 | 0.618 | 5.49 | 0.567 | 4.43 | 0.647 | | VoxCPM | 2.24 | 0.686 | **3.42** | 0.738 | 43.83 | 0.553 | 2.66 | 0.565 | 9.48 | 0.642 | | Qwen3-TTS | 3.38 | 0.684 | 4.60 | 0.726 | 79.14 | 0.528 | 5.81 | 0.527 | 17.59 | 0.626 | | **Raon-OpenTTS-0.3B** | 1.57 | 0.645 | 4.03 | 0.700 | 5.83 | 0.571 | **2.53** | 0.570 | 2.93 | 0.623 | | **Raon-OpenTTS-1B** | **1.44** | **0.718** | 3.51 | **0.769** | **5.61** | **0.656** | 2.77 | **0.633** | **2.81** | **0.695** | ## Splits ### CLEAN (2,500 pairs) Controlled read speech from studio and clean recording conditions. | Source | Pairs | License | |--------|------:|---------| | LibriSpeech-clean | 500 | CC BY 4.0 | | ST American English | 500 | CC BY-NC-ND 4.0 | | CMU-Arctic | 500 | BSD | | L2-ARCTIC | 500 | CC BY-NC 4.0 | | VCTK | 500 | CC BY 4.0 | ### NOISY (1,000 pairs) Read and prompted speech in the presence of background noise or reverberation. | Source | Pairs | License | |--------|------:|---------| | LibriSpeech-other | 500 | CC BY 4.0 | | TED-LIUM 3 | 500 | CC BY-NC-ND 4.0 | ### WILD (1,000 pairs) Unscripted conversational speech from real-world meetings captured under natural conditions. AMI-SDM samples are filtered to WER=0 to ensure transcription reliability. | Source | Pairs | License | |--------|------:|---------| | AMI-IHM | 500 | CC BY 4.0 | | AMI-SDM | 500 | CC BY 4.0 | ### EXPRESSIVE (1,500 pairs) Expressive speech covering a wide range of emotions and prosodic styles. | Source | Pairs | License | |--------|------:|---------| | CREMA-D | 500 | ODbL 1.0 | | EmoV-DB | 500 | Non-commercial research | | Expresso | 500 | CC BY-NC 4.0 | ## Licenses This dataset is a compilation of audio excerpts from multiple sources, each retaining its original license. The overall dataset is released under **CC BY-NC-ND 4.0** (the most restrictive license among the included sources). See the per-source table above for individual licenses. | License | Sources | |---------|---------| | CC BY 4.0 | LibriSpeech-clean, LibriSpeech-other, VCTK, AMI-IHM, AMI-SDM | | CC BY-NC 4.0 | L2-ARCTIC, Expresso | | CC BY-NC-ND 4.0 | ST American English, TED-LIUM 3 | | BSD | CMU-Arctic | | ODbL 1.0 | CREMA-D | | Non-commercial research | EmoV-DB | ## Citation ```bibtex @article{kim2026raonopentts, title = {Raon-OpenTTS: Open Models and Data for Robust Text-to-Speech}, author = {Kim, Semin and Chung, Seungjun and Moon, Taehong and Lee, Sangheon and Ahn, Minyoung and Lee, Keon and Kim, Nam Soo and Cho, Jaewoong and Schmidt, Ludwig and Lee, Kangwook and Park, Dongmin}, journal = {arXiv preprint arXiv:2605.20830}, year = {2026}, url = {https://arxiv.org/abs/2605.20830} } ``` © 2026 KRAFTON