| --- |
| license: cc-by-4.0 |
| task_categories: |
| - text-to-speech |
| language: |
| - en |
| tags: |
| - speech |
| - audio |
| - tts |
| - voice-cloning |
| - fish-speech |
| - synthetic |
| - maya-tts |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: "data/train-*" |
| --- |
| |
| # Maya TTS (maya-tts) |
|
|
| A high-fidelity expressive synthetic speech dataset for **Maya**, built using **Qwen2.5-1.5B-Instruct** for conversational dialogues and **Fish Audio S1-mini** for zero-shot voice cloning. |
|
|
| ## Dataset Overview |
|
|
| - **Total Clips:** 1518 |
| - **Total Duration:** 1.451 hours (87.07 minutes) |
| - **Audio Format:** WAV (embedded directly into Parquet shards via Hugging Face `Audio` feature) |
| - **Sampling Rate:** 44100 Hz |
| - **Language:** English (`en`) |
|
|
| ### Features |
| | Column | Type | Description | |
| | :--- | :--- | :--- | |
| | `id` | `string` | Unique zero-padded utterance identifier | |
| | `audio` | `Audio` | Embedded audio sample and decoding dict | |
| | `text` | `string` | Transcribed dialogue spoken by the cloned voice | |
| | `emotion` | `string` | Primary target emotion label (e.g. `happy`, `anxious`, `excited`) | |
| | `tag` | `string` | Specific delivery tone/special acoustic style (e.g. `whispering`, `shouting`) | |
| | `language` | `string` | Language code (`en`) | |
| | `duration` | `float` | Clip length in seconds | |
|
|
| ### Reference Speaker Information |
| - Target Voice: Cloned from single reference prompt (`ref_voice.wav`). |
| - **Reference Voice Transcript:** *"The fog had settled deep over the harbor long before midnight, turning the distant stone towers into jagged gray silhouettes against the sea. Out on the water, every sound carried double its daytime weight, the dull slap of cold black tide against the wooden pilings, the scrape of rusted iron chains, and the slow whistle of the gale driving through the cliffside pines. There is a particular stillness"* |
|
|
| ## Distribution & Statistics |
|
|
| ### Top Emotions |
| | Emotion | Count | |
| | :--- | :--- | |
| | `satisfied` | 45 | |
| | `worried` | 39 | |
| | `scared` | 39 | |
| | `painful` | 38 | |
| | `sad` | 38 | |
| | `disdainful` | 38 | |
| | `surprised` | 38 | |
| | `delighted` | 37 | |
| | `sincere` | 37 | |
| | `angry` | 37 | |
|
|
| ### Delivery Styles & Tone Tags |
| | Tag | Count | |
| | :--- | :--- | |
| | `sobbing` | 41 | |
| | `laughing` | 39 | |
| | `sighing` | 38 | |
| | `chuckling` | 35 | |
| | `in a hurry tone` | 35 | |
| | `soft tone` | 34 | |
| | `screaming` | 34 | |
| | `whispering` | 32 | |
| | `crying loudly` | 29 | |
| | `shouting` | 27 | |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load directly as an iterable audio stream |
| dataset = load_dataset("Shadow0482/maya-tts", split="train", streaming=True) |
| |
| for sample in dataset.take(2): |
| print(sample["id"], sample["text"]) |
| audio_array = sample["audio"]["array"] |
| sample_rate = sample["audio"]["sampling_rate"] |
| ``` |
|
|
| ## Generation Pipeline |
| - **Script Generation:** Prompted `Qwen/Qwen2.5-1.5B-Instruct` across multiple topics and affective directions. |
| - **Acoustic Synthesis:** Zero-shot target voice cloning via `fishaudio/openaudio-s1-mini`. |
| - **Validation:** Automatic duration, volume, and transcript alignment filtering. |
|
|
