Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,145 @@
+---
+language:
+  - en
+license: cc-by-4.0
+pretty_name: Clustered Reference Voices (EMOLIA 3K)
+size_categories:
+  - 1K<n<10K
+task_categories:
+  - audio-classification
+  - text-to-speech
+tags:
+  - speech
+  - voice-cloning
+  - speaker-embeddings
+  - speech-enhancement
+  - quality-scoring
+  - reference-voices
+dataset_info:
+  features:
+    - name: cluster_id
+      dtype: int64
+    - name: overall_quality
+      dtype: float64
+    - name: speech_quality
+      dtype: float64
+    - name: background_quality
+      dtype: float64
+    - name: content_enjoyment
+      dtype: float64
+    - name: duration
+      dtype: float64
+    - name: text
+      dtype: string
+    - name: sample_id
+      dtype: string
+    - name: cosine_similarity
+      dtype: float64
+  splits:
+    - name: train
+      num_examples: 3000
+---
+
+# Clustered Reference Voices (EMOLIA 3K)
+
+**3,000 enhanced reference voice MP3s** — one high-quality representative sample per speaker cluster, selected and scored by a multi-expert neural quality model.
+
+## Overview
+
+| Property | Value |
+|---|---|
+| **Total clips** | 3,000 |
+| **Total duration** | 11.3 hours |
+| **Mean duration** | 13.5 s (range: 3.5 – 29.9 s) |
+| **Format** | MP3, 192 kbps, 48 kHz |
+| **Language** | English |
+| **Naming** | `{cluster_id}.mp3` (0 – 2999) |
+
+## Source
+
+The source data is [laion/emolia-3k-speaker-clusters](https://huggingface.co/datasets/laion/emolia-3k-speaker-clusters), which contains **3,000 speaker clusters** with approximately 20 samples each (59,977 total utterances). Clusters were produced by grouping speaker embeddings from a diverse collection of English speech.
+
+## Processing Pipeline
+
+Each of the 59,977 source utterances was processed through a two-stage pipeline:
+
+### 1. Speech Enhancement — ClearerVoice MossFormer2_SE_48K
+
+All audio was enhanced at **48 kHz** using the [MossFormer2_SE_48K](https://github.com/modelscope/ClearerVoice-Enhancement) speech enhancement model. This removes background noise, music, reverb, and other non-speech artifacts while preserving the natural characteristics of the speaker's voice.
+
+### 2. Quality Scoring — Empathic Insight Voice Plus
+
+Enhanced audio was scored by the **Empathic Insight Voice Plus** model, which employs **59 MLP expert heads** on top of Whisper encoder embeddings. The model produces multiple quality dimensions:
+
+| Score | Description |
+|---|---|
+| `overall_quality` | Composite quality score (primary selection criterion) |
+| `speech_quality` | Clarity and naturalness of speech |
+| `background_quality` | Absence of background noise / artifacts |
+| `content_enjoyment` | Engaging and well-articulated content |
+
+### 3. Selection — Top Sample per Cluster
+
+For each of the 3,000 speaker clusters, the single sample with the **highest `overall_quality` score** was selected as the cluster's representative reference voice.
+
+## Quality Statistics
+
+| Metric | Overall Quality | Speech Quality | Background Quality | Content Enjoyment |
+|---|---|---|---|---|
+| **Mean** | 3.114 | 1.873 | 3.766 | 4.846 |
+| **Std** | 0.102 | 0.085 | 0.110 | 0.184 |
+| **Min** | 2.744 | 1.588 | 3.070 | 3.853 |
+| **Max** | 3.469 | 2.182 | 4.344 | 5.398 |
+
+## Dataset Files
+
+| File | Description | Size |
+|---|---|---|
+| `audio.tar.gz` | All 3,000 MP3 files | ~910 MB |
+| `metadata.parquet` | Quality scores and metadata for all clips | ~500 KB |
+| `gallery.html` | Interactive HTML gallery with embedded base64 audio, sortable columns, and search | ~5.5 MB |
+
+### Metadata Schema (parquet)
+
+| Column | Type | Description |
+|---|---|---|
+| `cluster_id` | int64 | Speaker cluster index (0–2999) |
+| `overall_quality` | float64 | Composite quality score |
+| `speech_quality` | float64 | Speech clarity / naturalness score |
+| `background_quality` | float64 | Background cleanliness score |
+| `content_enjoyment` | float64 | Content engagement score |
+| `duration` | float64 | Duration in seconds |
+| `text` | string | Transcript text |
+| `sample_id` | string | Original sample identifier |
+| `cosine_similarity` | float64 | Cosine similarity of sample's speaker embedding to cluster centroid |
+
+## Intended Uses
+
+- **TTS reference voices**: High-quality, diverse speaker references for text-to-speech systems
+- **Voice cloning**: Clean, enhanced single-speaker clips suitable as cloning targets
+- **Speaker verification benchmarks**: One representative per cluster for speaker ID tasks
+- **Quality filtering research**: Studying the relationship between quality scores and perceptual quality
+
+## Interactive Gallery
+
+The included `gallery.html` file provides a self-contained, browser-based interface to explore all 3,000 samples. Features:
+
+- Embedded base64 audio playback (no server required)
+- Sortable columns (click any header)
+- Full-text search across cluster IDs and transcripts
+- Quality score display for all dimensions
+
+## Citation
+
+```bibtex
+@dataset{clustered_reference_voices_2026,
+  title={Clustered Reference Voices (EMOLIA 3K)},
+  author={LAION},
+  year={2026},
+  url={https://huggingface.co/datasets/laion/clustered-reference-voices}
+}
+```
+
+## License
+
+This dataset is released under the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.