Rename RAON-TTS to Raon-OpenTTS throughout README

README.md

@@ -6,7 +6,7 @@ language:
   - en
 task_categories:
   - text-to-speech
-pretty_name: RAON-TTS-Pool
+pretty_name: Raon-OpenTTS-Pool
 size_categories:
   - 100M<n<1B
 configs:
@@ -78,7 +78,7 @@ configs:
         path: SPGISpeech2-Cut/metadata_core.parquet
 ---
 
-# RAON-TTS-Pool
+# Raon-OpenTTS-Pool
 
 <div align="center">
   <img class="block dark:hidden" src="assets/Raon-OpenTTS-Gradient-Black.png" alt="RAON-OpenTTS" width="400">
@@ -94,7 +94,7 @@ configs:
   <a href="#license"><img src="https://img.shields.io/badge/License-Mixed%20(see%20below)-lightgrey?style=flat" alt="License"></a>
 </p>
 
-**RAON-TTS-Pool** is a large-scale open English speech corpus for text-to-speech (TTS) training,
+**Raon-OpenTTS-Pool** is a large-scale open English speech corpus for text-to-speech (TTS) training,
 constructed from 8 publicly available speech corpora and a set of web-sourced recordings.
 It is the training data behind [RAON-OpenTTS](https://github.com/krafton-ai/RAON-OpenTTS),
 an open TTS model that performs on par with state-of-the-art closed-data systems.
@@ -111,8 +111,8 @@ with audio standardized to 16 kHz mono Opus 64 kbps for storage efficiency.
 The Raon-YouTube-Commons portion is reconstructed from [YouTube-Commons](https://huggingface.co/datasets/PleIAs/YouTube-Commons)
 through a dedicated preprocessing pipeline (see [below](#raon-youtube-commons)).
 
-With a model-based filtering pipeline applied to RAON-TTS-Pool, we derive
-**RAON-TTS-Core**, a curated high-quality subset of **510.1K hours** and **194.5M** segments.
+With a model-based filtering pipeline applied to Raon-OpenTTS-Pool, we derive
+**Raon-OpenTTS-Core**, a curated high-quality subset of **510.1K hours** and **194.5M** segments.
 
 For more details, see our paper: [Raon-OpenTTS: Open Models and Data for Robust Text-to-Speech](https://github.com/krafton-ai/RAON-OpenTTS)
 
@@ -133,11 +133,11 @@ Each WebDataset tar shard contains pairs of files per sample:
 Each dataset config has two metadata splits:
 
 - **pool** — all samples (sample_key, text, duration, shard_name)
-- **core** — quality-filtered subset (**RAON-TTS-Core**), retaining ~85% of the data
+- **core** — quality-filtered subset (**Raon-OpenTTS-Core**), retaining ~85% of the data
 
-### RAON-TTS-Core Filtering
+### Raon-OpenTTS-Core Filtering
 
-RAON-TTS-Core is constructed by applying three model-based quality filters and removing the bottom 15% of samples by combined score:
+Raon-OpenTTS-Core is constructed by applying three model-based quality filters and removing the bottom 15% of samples by combined score:
 
 1. **WER-based**: Transcribe each segment with Whisper-small ASR and compute WER against the existing text annotation. Samples with excessively high WER (> 0.35) indicate severe transcription mismatches.
 2. **DNSMOS-based**: Estimate perceptual speech quality using DNSMOS. Samples below 2.24 indicate strong background noise or distortion.
@@ -165,7 +165,7 @@ This combined filtering achieves the best overall TTS performance across diverse
 
 ### Raon-YouTube-Commons
 
-A substantial portion of RAON-TTS-Pool (335K hours) is derived from [YouTube-Commons](https://huggingface.co/datasets/PleIAs/YouTube-Commons).
+A substantial portion of Raon-OpenTTS-Pool (335K hours) is derived from [YouTube-Commons](https://huggingface.co/datasets/PleIAs/YouTube-Commons).
 Since the original release provides only YouTube URLs with noisy or unreliable transcriptions,
 we reconstructed it into a high-quality speech-text dataset through the following pipeline:
 
@@ -201,12 +201,12 @@ See [Preparing Non-redistributable Datasets](#preparing-non-redistributable-data
 from datasets import load_dataset
 
 # Core metadata for a single dataset
-meta = load_dataset("KRAFTON/RAON-TTS-Pool", "Raon-YouTube-Commons", split="core")
+meta = load_dataset("KRAFTON/Raon-OpenTTS-Pool", "Raon-YouTube-Commons", split="core")
 # Columns: sample_key, text, duration, shard_name
 print(meta[0])
 
 # All datasets combined
-all_core = load_dataset("KRAFTON/RAON-TTS-Pool", "all", split="core")
+all_core = load_dataset("KRAFTON/Raon-OpenTTS-Pool", "all", split="core")
 ```
 
 ### 2. Audio (WebDataset, local tars)
@@ -216,7 +216,7 @@ Download tars first:
 ```python
 from huggingface_hub import snapshot_download
 
-local_dir = snapshot_download("KRAFTON/RAON-TTS-Pool", repo_type="dataset",
+local_dir = snapshot_download("KRAFTON/Raon-OpenTTS-Pool", repo_type="dataset",
                                ignore_patterns=["*.parquet"])
 ```
 
@@ -247,7 +247,7 @@ import json, io, soundfile as sf
 
 # Step 1: load core sample keys from metadata
 core_keys = set(
-    load_dataset("KRAFTON/RAON-TTS-Pool", "LibriTTS-R", split="core")["sample_key"]
+    load_dataset("KRAFTON/Raon-OpenTTS-Pool", "LibriTTS-R", split="core")["sample_key"]
 )
 
 # Step 2: stream tars, skip non-core samples
@@ -268,7 +268,7 @@ for opus_bytes, json_bytes in dataset:
 ## Preparing Non-redistributable Datasets
 
 The script `prepare_nonredist_datasets.py` automatically downloads and converts GigaSpeech
-and SPGISpeech into the same WebDataset tar + parquet format used by RAON-TTS-Pool.
+and SPGISpeech into the same WebDataset tar + parquet format used by Raon-OpenTTS-Pool.
 
 ### Prerequisites
 
@@ -339,7 +339,7 @@ Available subsets: `L` (full ~5000h), `M` (~1000h), `S` (~200h), `dev`, `test`
 
 By default `metadata_core.parquet` equals `metadata_pool.parquet` since quality filtering
 requires an internal index file. If you have `pool_indices_filter_remove_15pct_combined.json`
-from the RAON-TTS maintainers, pass it with `--core_json` to generate a filtered core split.
+from the Raon-OpenTTS maintainers, pass it with `--core_json` to generate a filtered core split.
 
 ### Using with RAON-OpenTTS training