Duplicate from taresh18/AnimeVox

Co-authored-by: Taresh Rajput <taresh18@users.noreply.huggingface.co>

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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
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+*.jpg filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+dataset_info:
+  features:
+  - name: audio
+    dtype: audio
+  - name: transcription
+    dtype: string
+  - name: character_name
+    dtype: string
+  - name: anime
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 3431929153.84
+    num_examples: 11020
+  download_size: 3552115728
+  dataset_size: 3431929153.84
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+license: cc
+task_categories:
+- automatic-speech-recognition
+- text-to-speech
+language:
+- en
+pretty_name: 'AnimeVox: Character TTS Corpus'
+size_categories:
+- 10K<n<100K
+tags:
+- audio
+- text
+---
+
+# AnimeVox: Character TTS Corpus
+
+## 🗣️ Dataset Overview
+
+AnimeVox is an English Text-to-Speech (TTS) dataset featuring 11,020 audio clips from 19 distinct anime characters across popular series. Each clip includes a high-quality transcription, character name, and anime title, making it ideal for voice cloning, custom TTS model fine-tuning, and character voice synthesis research.
+
+The dataset was created and processed using **[TTSizer](https://github.com/taresh18/TTSizer)**, an open-source tool that automates creating high-quality TTS datasets from raw media (audio/video).
+
+**Watch the Demo Video:**
+[![AnimeVox Dataset Demo Video](https://img.youtube.com/vi/POwMVTwsZDQ/hqdefault.jpg)](https://youtu.be/POwMVTwsZDQ?si=rxNy7grLyROhdIEd)
+
+## 📊 Dataset Statistics
+
+- **Total samples:** 11,020
+- **Characters:** 19
+- **Anime series:** 15
+- **Audio format:** 44.1kHz mono WAV
+- **Storage size:** ~3.5GB
+
+## 🎧 Dataset Structure
+
+* **Instances:** Each sample is a dictionary with the following structure:
+  ```python
+  {
+    "audio": {"path": "...", "array": ..., "sampling_rate": 44100},
+    "transcription": "English text spoken by the character.",
+    "character_name": "Character Name",
+    "anime": "Anime Series Title"
+  }
+*   **Fields:**
+    *   `audio`: Audio object (44.1kHz).
+    *   `transcription`: (str) English transcription.
+    *   `character_name`: (str) Name of the speaking character.
+    *   `anime`: (str) Anime series title.
+*   **Splits:** A single train split with all 11,020 samples from 19 characters.
+
+## 🛠️ Dataset Creation
+
+### Source
+Audio clips were sourced from official English-dubbed versions of popular anime series. The clips were selected to capture diverse emotional tones and vocal characteristics unique to each character.
+
+### Processing with TTSizer
+This dataset was generated using **[TTSizer](https://github.com/taresh18/TTSizer)**, which offers an end-to-end automated pipeline for creating TTS-ready datasets. Key features utilized include:
+
+*   **Advanced Multi-Speaker Diarization:** To accurately identify and segment speech for each of the characters, even in complex audio environments.
+*   **State-of-the-Art Model Integration:** Leveraging models such as MelBandRoformer (for vocal separation), Gemini (for diarization), CTC-Aligner (for precise audio-text alignment), and WeSpeaker (for speaker embedding/verification).
+*   **Quality Control:** Implementing automatic outlier detection to flag and help refine potentially problematic audio-text pairs, ensuring higher dataset quality.
+
+The tool's configurable nature allowed for fine-tuning the entire process to suit the specific needs of this anime voice dataset.
+
+## 📜 Licensing & Usage
+
+*   **License:** Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).
+
+## 🚀 How to Use
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("taresh18/AnimeVox")
+
+# Access the training split
+train_data = dataset["train"]
+
+# Print dataset information
+print(f"Dataset contains {len(train_data)} samples")
+
+# Access a specific sample
+sample = train_data[0]
+print(f"Character: {sample['character_name']}")
+print(f"From anime: {sample['anime']}")
+print(f"Transcription: {sample['transcription']}")
+```

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