| --- |
| license: apache-2.0 |
| language: |
| - mos |
| - fr |
| tags: |
| - text-to-speech |
| - tts |
| - moore |
| - burkina-faso |
| - african-languages |
| - voice-cloning |
| - speech-synthesis |
| pipeline_tag: text-to-speech |
| --- |
| |
| # BIA-SPARKTTS-V2 |
|
|
| ## Model Description |
|
|
| BIA-SPARKTTS-V2 is a fine-tuned version of SparkTTS specifically optimized for Moore language text-to-speech synthesis. This model enables high-quality speech generation in Moore with voice cloning capabilities, supporting the linguistic needs of Burkina Faso and the wider Moore-speaking community. |
|
|
| Our model is built upon the powerful SparkTTS architecture, leveraging state-of-the-art neural vocoding and voice cloning techniques. It has been fine-tuned on curated Moore speech data to ensure natural and contextually appropriate speech synthesis for Moore speakers. |
|
|
| ## Language Information |
|
|
| - Primary Language: Moore (Mooré) |
| - Language Code: `moor_Latn` |
| - Speech Synthesis: Text-to-Speech (TTS) |
| - Region: Burkina Faso, West Africa |
| - Speakers: ~5 million native speakers |
| - Language Family: Niger-Congo → Gur → Oti-Volta → Moore |
|
|
| ## Model Performance |
|
|
| The model achieves the following results on the evaluation set: |
|
|
| - Step 1000: Training Loss: 4.158300 | Validation Loss: 4.068496 |
| - Step 2000: Training Loss: 3.816300 | Validation Loss: 3.789840 |
| - Step 3000: Training Loss: 3.655900 | Validation Loss: 3.688933 |
| - Step 4000: Training Loss: 3.609400 | Validation Loss: 3.628450 |
| - Step 5000: Training Loss: 3.528000 | Validation Loss: 3.593397 |
| - Step 6000: Training Loss: 3.463600 | Validation Loss: 3.563343 |
| - Step 7000: Training Loss: 3.404400 | Validation Loss: 3.546153 |
| - Step 8000: Training Loss: 3.395500 | Validation Loss: 3.528718 |
| - Step 9000: Training Loss: 3.367900 | Validation Loss: 3.524420 |
| - Step 10000: Training Loss: 3.359900 | Validation Loss: 3.522905 |
| - Parameters: 0.5B (500M) |
| - Total Training Steps: 10,680 (8 epochs) |
| - Sample Rate: 16kHz |
| - Audio Quality: Natural, clear pronunciation |
|
|
| ## Features |
|
|
| - **Voice Creation**: Generate speech with customizable gender, pitch, and speed |
| - **Voice Cloning**: Clone voices from audio samples (male/female) |
| - **Custom Voice Cloning**: Upload your own reference audio for personalized voice cloning |
| - **Flexible Generation**: Adjustable temperature, top-k, and top-p sampling parameters |
|
|
| ## Usage |
|
|
| ### Installation |
|
|
| ```bash |
| pip install torch transformers gradio soundfile |
| pip install git+https://github.com/SparkAudio/SparkTTS.git |
| ``` |
|
|
| ### Basic Usage |
|
|
| ```python |
| import torch |
| from SparkTTS import SparkTTS |
| |
| # Initialize model |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" |
| model = SparkTTS("burkimbia/BIA-SPARKTTS-V1", device=device) |
| |
| # Generate speech with voice creation |
| text = "Yʋʋmd fãa, a ɛlkana rag n yita a tẽngẽ wã" |
| wav = model.inference( |
| text, |
| gender="male", |
| pitch="moderate", |
| speed="moderate", |
| do_sample=True, |
| temperature=0.8, |
| top_k=50, |
| top_p=0.95 |
| ) |
| |
| # Save audio |
| import soundfile as sf |
| sf.write("output.wav", wav, 16000) |
| ``` |
|
|
| ### Voice Cloning with Audio Sample |
|
|
| ```python |
| # Clone voice from reference audio |
| reference_audio = "path/to/reference/audio.wav" |
| wav = model.inference( |
| text, |
| prompt_speech_path=reference_audio, |
| prompt_text=None, # Optional: transcript of reference audio |
| do_sample=True, |
| temperature=0.8, |
| top_k=50, |
| top_p=0.95 |
| ) |
| ``` |
|
|
| ### Advanced Parameters |
|
|
| ```python |
| # Fine-tune generation with custom parameters |
| wav = model.inference( |
| text, |
| gender="female", |
| pitch="high", # very_low | low | moderate | high | very_high |
| speed="moderate", # very_low | low | moderate | high | very_high |
| do_sample=True, # Enable sampling for more natural variation |
| temperature=0.8, # Higher = more creative (0.1-1.5) |
| top_k=50, # Number of tokens to consider (1-100) |
| top_p=0.95, # Nucleus sampling threshold (0.1-1.0) |
| ) |
| ``` |
|
|
| ## Model Details |
|
|
| ### Training Configuration |
| - Base Model: SparkTTS-0.5B |
| - Model Size: 500M parameters |
| - Training Steps: 10,680 steps (8 epochs) |
| - Batch Size: 32 per device |
| - Gradient Accumulation Steps: 2 |
| - Effective Batch Size: 64 |
| - Learning Rate: 5e-5 |
| - LR Scheduler: Cosine with 500 warmup steps |
| - Optimizer: AdamW 8-bit with 0.01 weight decay |
| - Sample Rate: 16kHz |
| - Max Tokens: 3000 |
| - Hardware: NVIDIA A100-SXM4-40GB (40GB VRAM) |
| - Framework: PyTorch (FP32) |
| - Seed: 2025 |
|
|
| ### Training Progress |
| The model showed consistent improvement during training: |
| - Rapid initial improvement (4.07 → 3.79 validation loss in first 2000 steps) |
| - Steady convergence from 3.69 to 3.52 over 10,000 steps |
| - Best model checkpoint saved at step 10,000 (validation loss: 3.522905) |
| - Evaluation frequency: Every 1000 steps |
| - Total training time: ~12-14 hours on A100 |
|
|
| ### Architecture Components |
| - **Audio Tokenizer**: BiCodec for high-quality audio encoding |
| - **Language Model**: Causal LM for sequential token generation |
| - **Vocoder**: Neural vocoder for waveform synthesis |
| - **Voice Encoder**: For voice cloning and style transfer |
|
|
| ## Generation Parameters |
|
|
| ### do_sample |
| - `True`: Enables sampling for natural variation (recommended) |
| - `False`: Greedy decoding for deterministic output |
| |
| ### temperature |
| - Range: 0.1 - 1.5 |
| - Default: 0.8 |
| - Lower values (0.3-0.5): More deterministic, consistent pronunciation |
| - Higher values (0.8-1.2): More natural variation and expressiveness |
| |
| ### top_k |
| - Range: 1 - 100 |
| - Default: 50 |
| - Number of highest probability tokens to consider during sampling |
|
|
| ### top_p (nucleus sampling) |
| - Range: 0.1 - 1.0 |
| - Default: 0.95 |
| - Cumulative probability threshold for token selection |
| |
| ## Limitations |
| |
| - The model performs best on standard Moore text with proper orthography |
| - Extremely long texts may require chunking |
| - Voice quality depends on reference audio quality for cloning |
| - Generated speech may vary slightly between runs when sampling is enabled |
| - Specialized technical terms may require phonetic adjustments |
| |
| ## Use Cases |
| |
| - **Education**: Moore language learning and pronunciation |
| - **Accessibility**: Text-to-speech for visually impaired Moore speakers |
| - **Content Creation**: Audiobooks, podcasts, and media in Moore |
| - **Communication**: Voice assistants and chatbots for Moore speakers |
| - **Preservation**: Digital archiving of Moore language resources |
| |
| ## Gradio Demo |
| |
| A web interface is available for easy testing: |
| |
| ```python |
| import gradio as gr |
| from SparkTTS import SparkTTS |
| |
| model = SparkTTS("burkimbia/BIA-SPARKTTS-V1") |
| |
| def generate_speech(text, gender, pitch, speed): |
| wav = model.inference(text, gender=gender, pitch=pitch, speed=speed) |
| return (16000, wav) |
| |
| demo = gr.Interface( |
| fn=generate_speech, |
| inputs=[ |
| gr.Textbox(label="Moore Text"), |
| gr.Radio(["male", "female"], label="Gender"), |
| gr.Slider(1, 5, step=1, value=3, label="Pitch"), |
| gr.Slider(1, 5, step=1, value=3, label="Speed"), |
| ], |
| outputs=gr.Audio(label="Generated Speech") |
| ) |
| |
| demo.launch() |
| ``` |
| |
| ## Feedback |
| |
| We're continuously striving to improve our model's performance and usability. If you have any feedback, suggestions, or encounter any issues, please don't hesitate to reach out to us at [BurkimbIA](https://huggingface.co/burkimbia). |
| |
| ## Citation |
| |
| If you use this model in your research, please cite: |
| |
| ```bibtex |
| @misc{bia-sparktts-v1, |
| title={BIA-SPARKTTS-V2: A Fine-tuned SparkTTS Model for Moore Language Speech Synthesis}, |
| author={BurkimbIA}, |
| year={2025}, |
| howpublished={\url{https://huggingface.co/burkimbia/BIA-SPARKTTS-V1}} |
| } |
| ``` |
| |
| ## Acknowledgments |
| |
| - Coqui tts team for the SparkTTS architecture and pretrained models |
| - Speech data contributors who provided high-quality recordings |
| - BurkimbIA team for data curation and model development |
| |
| ## Framework Versions |
| |
| - Transformers: 4.57.1 |
| - PyTorch: 2.2.1+cu121 |
| - Python: 3.12+ |
| - Gradio: 5.0+ |
| |
| |
| Developed by [BurkimbIA](https://huggingface.co/burkimbia) - Advancing African Language Technologies |
| |
