ARIA
88e07487ee
- Added generate_stream() method for token-by-token streaming - Added generate_and_play() method for real-time playback - Added decode_chunk() to ncodec codec - First audio chunk in ~180ms (390% faster than non-streaming) - Updated README with streaming documentation
90 lines
3.6 KiB
Plaintext
90 lines
3.6 KiB
Plaintext
Metadata-Version: 2.4
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Name: FastNeuTTS
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Version: 0.0.11
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Summary: High quality and Fast TTS with MiraTTS
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Author-email: Yatharth Sharma <yatharthsharma3501@gmail.com>
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Project-URL: Homepage, https://github.com/ysharma3501/MiraTTS
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Project-URL: Issues, https://github.com/ysharma3501/MiraTTS/issues
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Classifier: Programming Language :: Python :: 3
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Classifier: License :: OSI Approved :: MIT License
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Classifier: Operating System :: OS Independent
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Requires-Python: >=3.10
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Description-Content-Type: text/markdown
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Requires-Dist: lmdeploy
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Requires-Dist: librosa
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Requires-Dist: fastaudiosr @ git+https://github.com/ysharma3501/FlashSR.git
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Requires-Dist: ncodec @ git+https://github.com/ysharma3501/FastBiCodec.git
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Requires-Dist: einops
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Requires-Dist: onnxruntime-gpu
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# MiraTTS
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[MiraTTS](https://huggingface.co/YatharthS/MiraTTS) is a finetune of the excellent [Spark-TTS](https://huggingface.co/SparkAudio/Spark-TTS-0.5B) model for enhanced realism and stability performing on par with closed source models.
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This repository also heavily optimizes Mira with [Lmdeploy](https://github.com/InternLM/lmdeploy) and boosts quality by using [FlashSR](https://github.com/ysharma3501/FlashSR) to generate high quality audio at over **100x** realtime!
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https://github.com/user-attachments/assets/262088ae-068a-49f2-8ad6-ab32c66dcd17
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## Key benefits
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- Incredibly fast: Over 100x realtime by using Lmdeploy and batching.
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- High quality: Generates clear and crisp 48khz audio outputs which is much higher quality then most models.
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- Memory efficient: Works within 6gb vram.
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- Low latency: Latency can be low as 100ms.
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## Usage
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Simple 1 line installation:
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```
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uv pip install git+https://github.com/ysharma3501/MiraTTS.git
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```
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Running the model(bs=1):
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```python
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from mira.model import MiraTTS
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from IPython.display import Audio
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mira_tts = MiraTTS('YatharthS/MiraTTS') ## downloads model from huggingface
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file = "reference_file.wav" ## can be mp3/wav/ogg or anything that librosa supports
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text = "Alright, so have you ever heard of a little thing named text to speech? Well, it allows you to convert text into speech! I know, that's super cool, isn't it?"
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context_tokens = mira_tts.encode_audio(file)
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audio = mira_tts.generate(text, context_tokens)
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Audio(audio, rate=48000)
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```
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Running the model using batching:
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```python
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file = "reference_file.wav" ## can be mp3/wav/ogg or anything that librosa supports
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text = ["Hey, what's up! I am feeling SO happy!", "Honestly, this is really interesting, isn't it?"]
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context_tokens = [mira_tts.encode_audio(file)]
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audio = mira_tts.batch_generate(text, context_tokens)
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Audio(audio, rate=48000)
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```
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Examples can be seen in the [huggingface model](https://huggingface.co/YatharthS/MiraTTS)
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I recommend reading these 2 blogs to better easily understand LLM tts models and how I optimize them
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- How they work: https://huggingface.co/blog/YatharthS/llm-tts-models
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- How to optimize them: https://huggingface.co/blog/YatharthS/making-neutts-200x-realtime
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## Training
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Released training code! You can now train the model to be multilingual, multi-speaker, or support audio events on any local or cloud gpu!
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Kaggle notebook: https://www.kaggle.com/code/yatharthsharma888/miratts-training
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Colab notebook: https://colab.research.google.com/drive/1IprDyaMKaZrIvykMfNrxWFeuvj-DQPII?usp=sharing
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## Next steps
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- [x] Release code and model
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- [x] Release training code
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- [ ] Support low latency streaming
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- [ ] Release native 48khz bicodec
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## Final notes
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Thanks very much to the authors of Spark-TTS and unsloth. Thanks for checking out this repository as well.
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Stars would be well appreciated, thank you.
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Email: yatharthsharma3501@gmail.com
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