initial commit

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README.md

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+---
+license: cc-by-nc-nd-4.0
+tags:
+  - tts
+  - text-to-speech
+  - zero-shot-tts
+  - evaluation
+  - benchmark
+  - speech
+pretty_name: Raon-TTS-Eval
+size_categories:
+  - 1K<n<10K
+task_categories:
+  - text-to-speech
+language:
+  - en
+---
+
+<div align="center">
+  <img class="block dark:hidden" src="assets/Raon-OpenTTS-Gradient-Black.png" alt="Raon OpenTTS" width="600">
+  <img class="hidden dark:block" src="assets/Raon-OpenTTS-Gradient-White.png" alt="Raon OpenTTS" width="600">
+</div>
+
+# Raon-TTS-Eval
+
+A robustness-oriented evaluation benchmark for zero-shot text-to-speech, covering **4 acoustic regimes** (Clean, Noisy, Wild, Emotional) 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-TTS-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-TTS-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/
+└── emotional/
+    ├── metadata.csv      # 1,500 pairs
+    └── audio/
+```
+
+Each `metadata.csv` has the following columns:
+
+| Column | Description |
+|--------|-------------|
+| `category` | Acoustic regime (CLEAN / NOISY / WILD / EMOTIONAL) |
+| `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-TTS-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`, `emotional` / `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
+  emotional    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
+
+TBD
+
+## 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 |
+
+### EMOTIONAL (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{raon2026opentts,
+  title={RAON-OpenTTS: Open Models and Data for Robust Text-to-Speech},
+  author={...},
+  year={2026}
+}
+```

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