11mlabs
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indri-0.1-350m-tts

@@ -1,199 +1,176 @@
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 ---
+license: cc-by-sa-4.0
+datasets:
+- speechcolab/gigaspeech
+- parler-tts/mls_eng_10k
+- reach-vb/jenny_tts_dataset
+- MikhailT/hifi-tts
+- ylacombe/expresso
+- keithito/lj_speech
+- collabora/ai4bharat-shrutilipi
+language:
+- en
+- hi
+base_model:
+- openai-community/gpt2
+pipeline_tag: text-to-speech
 library_name: transformers
 ---
+| Platform | Link |
+|----------|------|
+| 🌎 Live Demo | [indrivoice.ai](https://indrivoice.ai/) |
+| 𝕏 Twitter | [@11mlabs](https://x.com/11mlabs) |
+| 🐱 GitHub | [Indri Repository](https://github.com/cmeraki/indri) |
+| 🤗 Hugging Face (Collection) | [Indri collection](https://huggingface.co/collections/11mlabs/indri-673dd4210b4369037c736bfe) |
+| 📝 Release Blog | [Release Blog](https://www.indrivoice.ai/blog/2024-11-21-building-indri-tts) |
+# Model Card for indri-0.1-350m-tts
+Indri is a series of audio models that can do TTS, ASR, and audio continuation. This is the medium sized model (350M) in our series and supports TTS tasks in 2 languages:
+1. English
+2. Hindi
 ## Model Details
 ### Model Description
+`indri-0.1-350m-tts` is a novel, ultra-small, and lightweight TTS model based on the transformer architecture.
+It models audio as tokens and can generate high-quality audio with consistent style cloning of the speaker.
+### Samples
+| Text | Sample |
+| --- | --- |
+|अतीत गौरवशाली, वर्तमान आशावादी, भविष्य उज्जवल| <audio controls src="https://huggingface.co/11mlabs/indri-0.1-124m-tts/resolve/main/data/417f5f1b-d641-4393-b922-9da9644dcd1b.wav" title="Title"></audio> |
+|भाइयों और बहनों, ये हमारा सौभाग्य है कि हम सब मिलकर इस महान देश को नई ऊंचाइयों पर ले जाने का सपना देख रहे हैं।| <audio controls src="https://huggingface.co/11mlabs/indri-0.1-124m-tts/resolve/main/data/6e0a4879-0379-4166-a52c-03220a3f2922.wav" title="Title"></audio> |
+|Hello दोस्तों, future of speech technology mein अपका स्वागत है | <audio controls src="https://huggingface.co/11mlabs/indri-0.1-124m-tts/resolve/main/data/5848b722-efe3-4e1f-a15e-5e7d431cd475.wav" title="Title"></audio> |
+|Artificial Intelligence's collaborative hub: Transforming Machine Learning together| <audio controls src="https://huggingface.co/11mlabs/indri-0.1-124m-tts/resolve/main/data/12e5a00e-834b-4c3c-a8b8-7f545ba7088c.wav" title="Title"></audio> |
+|Intelligent machines processing data at lightning-fast electronic speeds| <audio controls src="https://huggingface.co/11mlabs/indri-0.1-124m-tts/resolve/main/data/e21efa09-e179-42b7-982a-b686038a8f60.wav" title="Title"></audio> |
+### Key features
+1. Extremely efficient, based on GPT-2 medium architecture. The methodology can be extended to any autoregressive transformer-based architecture.
+2. Supports voice cloning with small prompts (<5s).
+3. Code mixing text input in 2 languages - English and Hindi.
+4. Ultra-fast. Can generate 5 seconds of audio per second on Amphere generation NVIDIA GPUs, and up to 10 seconds of audio per second on Ada generation NVIDIA GPUs.
+### Details
+1. Model Type: GPT-2 based language model
+2. Size: 350M parameters
+3. Language Support: English, Hindi
+4. License: CC BY 4.0
+### Speed
+## Technical details
+Here's a brief of how the model works:
+1. Converts input text into tokens
+2. Runs autoregressive decoding on GPT-2 based transformer model and generates audio tokens
+3. Decodes audio tokens (using [Kyutai/mimi](https://huggingface.co/kyutai/mimi)) to audio
+Please read our blog [here](https://www.indrivoice.ai/blog/2024-11-21-building-indri-tts) for more technical details on how it was built.
 ## How to Get Started with the Model
+### 🤗 pipelines
+Use the code below to get started with the model. Pipelines are the best way to get started with the model.
+```python
+import torch
+import torchaudio
+from transformers import pipeline
+model_id = '11mlabs/indri-0.1-350m-tts'
+task = 'indri-tts'
+pipe = pipeline(
+    task,
+    model=model_id,
+    device=torch.device('cuda:0'), # Update this based on your hardware,
+    trust_remote_code=True
+)
+output = pipe(['Hi, my name is Indri and I like to talk.'])
+torchaudio.save('output.wav', output[0]['audio'][0], sample_rate=24000)
+```
+### Self hosted service
+```bash
+git clone https://github.com/cmeraki/indri.git
+cd indri
+pip install -r requirements.txt
+# Install ffmpeg (for Mac/Windows, refer here: https://www.ffmpeg.org/download.html)
+sudo apt update -y
+sudo apt upgrade -y
+sudo apt install ffmpeg -y
+python -m inference --model_path 11mlabs/indri-0.1-350m-tts --device cuda:0 --port 8000
+```
+## Citation
+If you use this model in your research, please cite:
+```bibtex
+@misc{indri-multimodal-alm,
+  author       = {11mlabs},
+  title        = {Indri: Multimodal audio language model},
+  year         = {2024},
+  publisher    = {GitHub},
+  journal      = {GitHub Repository},
+  howpublished = {\url{https://github.com/cmeraki/indri}},
+  email        = {[email protected]}
+}
+```
+## BibTex
+1. [nanoGPT](https://github.com/karpathy/nanoGPT)
+2. [Kyutai/mimi](https://huggingface.co/kyutai/mimi)
+```bibtex
+@techreport{kyutai2024moshi,
+      title={Moshi: a speech-text foundation model for real-time dialogue},
+      author={Alexandre D\'efossez and Laurent Mazar\'e and Manu Orsini and
+      Am\'elie Royer and Patrick P\'erez and Herv\'e J\'egou and Edouard Grave and Neil Zeghidour},
+      year={2024},
+      eprint={2410.00037},
+      archivePrefix={arXiv},
+      primaryClass={eess.AS},
+      url={https://arxiv.org/abs/2410.00037},
+}
+```
+3. [Whisper](https://github.com/openai/whisper)
+```bibtex
+@misc{radford2022whisper,
+  doi = {10.48550/ARXIV.2212.04356},
+  url = {https://arxiv.org/abs/2212.04356},
+  author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
+  title = {Robust Speech Recognition via Large-Scale Weak Supervision},
+  publisher = {arXiv},
+  year = {2022},
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+```
+4. [silero-vad](https://github.com/snakers4/silero-vad)
+```bibtex
+@misc{Silero VAD,
+  author = {Silero Team},
+  title = {Silero VAD: pre-trained enterprise-grade Voice Activity Detector (VAD), Number Detector and Language Classifier},
+  year = {2024},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/snakers4/silero-vad}},
+  commit = {insert_some_commit_here},
+  email = {[email protected]}
+}
+```