feat: add streaming support for real-time TTS

- 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
2026-03-22 04:40:37 +01:00
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+# MiraTTS
+[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. 
+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!
+
+https://github.com/user-attachments/assets/262088ae-068a-49f2-8ad6-ab32c66dcd17
+
+## Key benefits
+- Incredibly fast: Over 100x realtime by using Lmdeploy and batching.
+- High quality: Generates clear and crisp 48khz audio outputs which is much higher quality then most models.
+- Memory efficient: Works within 6gb vram.
+- Low latency: Latency can be low as 100ms.
+
+## Usage
+Simple 1 line installation:
+```
+uv pip install git+https://github.com/ysharma3501/MiraTTS.git
+```
+
+Running the model(bs=1):
+```python
+from mira.model import MiraTTS
+from IPython.display import Audio
+mira_tts = MiraTTS('YatharthS/MiraTTS') ## downloads model from huggingface
+
+file = "reference_file.wav" ## can be mp3/wav/ogg or anything that librosa supports
+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?"
+
+context_tokens = mira_tts.encode_audio(file)
+audio = mira_tts.generate(text, context_tokens)
+
+Audio(audio, rate=48000)
+```
+
+Running the model using batching: 
+```python
+file = "reference_file.wav" ## can be mp3/wav/ogg or anything that librosa supports
+text = ["Hey, what's up! I am feeling SO happy!", "Honestly, this is really interesting, isn't it?"]
+
+context_tokens = [mira_tts.encode_audio(file)]
+
+audio = mira_tts.batch_generate(text, context_tokens)
+
+Audio(audio, rate=48000)
+```
+
+## Streaming (Real-time Audio)
+
+Stream audio chunks as they're generated for ultra-low latency (~180ms to first audio):
+
+```python
+from mira.model import MiraTTS
+
+mira_tts = MiraTTS('YatharthS/MiraTTS')
+context_tokens = mira_tts.encode_audio("reference_file.wav")
+
+# Stream and process chunks in real-time
+for audio_chunk in mira_tts.generate_stream(text, context_tokens, chunk_size=50):
+    # audio_chunk is a torch tensor (48kHz)
+    # Process/play each chunk as it arrives
+    process(audio_chunk)
+```
+
+Or use the convenience method for immediate playback (requires `sounddevice`):
+
+```python
+# pip install sounddevice
+mira_tts.generate_and_play(text, context_tokens, chunk_size=50)
+```
+
+**Parameters:**
+- `chunk_size`: Tokens per chunk (default 50 = ~1 sec audio). Lower = faster first chunk, higher = smoother audio.
+
+**Performance:**
+- First audio chunk: ~180ms (vs ~870ms for full generation)
+- 390% faster time to first audio
+
+Examples can be seen in the [huggingface model](https://huggingface.co/YatharthS/MiraTTS)
+
+I recommend reading these 2 blogs to better easily understand LLM tts models and how I optimize them
+- How they work: https://huggingface.co/blog/YatharthS/llm-tts-models
+- How to optimize them: https://huggingface.co/blog/YatharthS/making-neutts-200x-realtime
+
+## Training
+Released training code! You can now train the model to be multilingual, multi-speaker, or support audio events on any local or cloud gpu!
+
+Kaggle notebook: https://www.kaggle.com/code/yatharthsharma888/miratts-training
+
+Colab notebook: https://colab.research.google.com/drive/1IprDyaMKaZrIvykMfNrxWFeuvj-DQPII?usp=sharing
+
+## Next steps
+- [x] Release code and model
+- [x] Release training code
+- [x] Support low latency streaming
+- [ ] Release native 48khz bicodec
+      
+## Final notes
+Thanks very much to the authors of Spark-TTS and unsloth. Thanks for checking out this repository as well.
+
+Stars would be well appreciated, thank you.
+
+Email: yatharthsharma3501@gmail.com