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--- |
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license: agpl-3.0 |
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pipeline_tag: text-to-speech |
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library_name: gen |
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tags: |
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- text-to-speech |
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- audio |
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- speech |
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- voice-cloning |
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- vc |
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- tts |
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--- |
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![MARS5 Banner](assets/github-banner.png) |
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# MARS5: A novel speech model for insane prosody. |
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This is the repo for the MARS5 English speech model (TTS) from CAMB.AI. |
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The model follows a two-stage AR-NAR pipeline with a distinctively novel NAR component (see more info in the [docs](docs/architecture.md)). |
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With just 5 seconds of audio and a snippet of text, MARS5 can generate speech even for prosodically hard and diverse scenarios like sports commentary, anime and more. Check out our demo: |
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https://github.com/Camb-ai/MARS5-TTS/assets/23717819/3e191508-e03c-4ff9-9b02-d73ae0ebefdd |
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**Quick links**: |
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- [CAMB.AI website](https://camb.ai/) (access MARS5 in 140+ languages for TTS and dubbing) |
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- Technical docs: [in the docs folder](docs/architecture.md) |
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- Colab quickstart: <a target="_blank" href="https://colab.research.google.com/github/Camb-ai/mars5-tts/blob/master/mars5_demo.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
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- Demo page with samples: [here](https://179c54d254f7.ngrok.app/) |
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![Mars 5 simplified diagram](docs/assets/simplified_diagram.png) |
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**Figure**: the high-level architecture flow of Mars 5. Given text and a reference audio, coarse (L0) encodec speech features are obtained through an autoregressive transformer model. Then, the text, reference, and coarse features are refined in a multinomial DDPM model to produce the remaining encodec codebook values. The output of the DDPM is then vocoded to produce the final audio. |
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Because the model is trained on raw audio together with byte-pair-encoded text, it can be steered with things like punctuation and capitalization. |
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E.g. to add a pause, add a comma to that part in the transcript. Or, to emphasize a word, put it in capital letters in the transcript. |
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This enables a fairly natural way for guiding the prosody of the generated output. |
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Speaker identity is specified using an audio reference file between 2-12 seconds, with lengths around 6s giving optimal results. |
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Further, by providing the transcript of the reference, MARS5 enables one to do a '_deep clone_' which improves the quality of the cloning and output, at the cost of taking a bit longer to produce the audio. |
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For more details on this and other performance and model details, please see inside the [docs folder](docs/architecture.md). |
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## Quickstart |
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We use `torch.hub` to make loading the model easy -- no cloning of the repo needed. The steps to perform inference are simple: |
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1. **Install pip dependencies**: we have 3 inference dependencies only `torch`, `torchaudio`, `librosa`, `vocos`, and `encodec`. Python must be at version 3.10 or greater, and torch must be v2.0 or greater. |
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```bash |
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pip install --upgrade torch torchaudio librosa vocos encodec |
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``` |
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2. **Load models**: load the Mars 5 AR and NAR model from torch hub: |
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```python |
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import torch, librosa |
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mars5, config_class = torch.hub.load('Camb-ai/mars5-tts', 'mars5_english', trust_repo=True) |
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# The `mars5` contains the AR and NAR model, as well as inference code. |
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# The `config_class` contains tunable inference config settings like temperature. |
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``` |
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3. **Pick a reference** and optionally its transcript: |
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```python |
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# load reference audio between 1-12 seconds. |
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wav, sr = librosa.load('<path to arbitrary 24kHz waveform>.wav', |
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sr=mars5.sr, mono=True) |
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wav = torch.from_numpy(wav) |
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ref_transcript = "<transcript of the reference audio>" |
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``` |
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The reference transcript is an optional piece of info you need if you wish to do a deep clone. |
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Mars5 supports 2 kinds of inference: a shallow, fast inference whereby you do not need the transcript of the reference (we call this a _shallow clone_), and a second slower, but typically higher quality way, which we call a _deep clone_. |
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To use the deep clone, you need the prompt transcript. See the [model docs](docs/architecture.md) for more info on this. |
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4. **Perform the synthesis**: |
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```python |
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# Pick whether you want a deep or shallow clone. Set to False if you don't know prompt transcript or want fast inference. Set to True if you know transcript and want highest quality. |
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deep_clone = True |
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# Below you can tune other inference settings, like top_k, temperature, top_p, etc... |
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cfg = config_class(deep_clone=deep_clone, rep_penalty_window=100, |
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top_k=100, temperature=0.7, freq_penalty=3) |
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ar_codes, output_audio = mars5.tts("The quick brown rat.", wav, |
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ref_transcript, |
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cfg=cfg) |
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# output_audio is (T,) shape float tensor corresponding to the 24kHz output audio. |
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``` |
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That's it! These default settings provide pretty good results, but feel free to tune the inference settings to optimize the output for your particular example. See the [`InferenceConfig`](inference.py) code or the demo notebook for info and docs on all the different inference settings. |
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_Some tips for best quality:_ |
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- Make sure reference audio is clean and between 1 second and 12 seconds. |
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- Use deep clone and provide an accurate transcript for the reference. |
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- Use proper punctuation -- the model can be guided and made better or worse with proper use of punctuation and capitalization. |
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## Model details |
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**Checkpoints** |
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The checkpoints for MARS5 are provided under the releases tab of this github repo. We provide two checkpoints: |
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- AR fp16 checkpoint [~750M parameters], along with config embedded in the checkpoint. |
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- NAR fp16 checkpoint [~450M parameters], along with config embedded in the checkpoint. |
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- The byte-pair encoding tokenizer used for the L0 encodec codes and the English text is embedded in each checkpoint under the `'vocab'` key, and follows roughly the same format of a saved minbpe tokenizer. |
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**Hardware requirements**: |
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You must be able to store at least 750M+450M params on GPU, and do inference with 750M of active parameters. In general, at least **20GB of GPU VRAM** is needed to run the model on GPU (we plan to further optimize this in the future). |
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If you do not have the necessary hardware requirements and just want to use MARS5 in your applications, you can use it via our API: see [docs.camb.ai](https://docs.camb.ai/). If you need some more credits to test it for your use case, feel free to reach out to `help@camb.ai` for help. |
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## Roadmap |
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Mars 5 is not perfect at the moment, and we are working on a few efforts to improve its quality, stability, and performance. |
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Rough areas we are looking to improve, and welcome any contributions: |
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- Improving inference stability and consistency |
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- Speed/performance optimizations |
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- Improving reference audio selection when given long references. |
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- Benchmark performance numbers for Mars 5 on standard speech datasets. |
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If you would like to contribute any improvement to MARS, please feel free to contribute (guidelines below). |
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## Contributions |
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We welcome any contributions to improving the model. As you may find when experimenting, it can produce really great results, it can still be further improved to create excellent outputs _consistently_. |
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**Contribution format**: |
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The preferred way to contribute to our repo is to fork the [master repository](https://github.com/Camb-ai/mars5-tts) on GitHub: |
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1. Fork the repo on github |
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2. Clone the repo, set upstream as this repo: `git remote add upstream git@github.com:Camb-ai/mars5-tts.git` |
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3. Make to a new local branch and make your changes, commit changes. |
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4. Push changes to new upstream branch: `git push --set-upstream origin <NAME-NEW-BRANCH>` |
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5. On github, go to your fork and click 'Pull request' to begin the PR process. Please make sure to include a description of what you did/fixed. |
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## License |
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We are open-sourcing MARS in English under GNU AGPL 3.0, but you can request to use it under a different license by emailing help@camb.ai |
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## Join our team |
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We're an ambitious team, globally distributed, with a singular aim of making everyone's voice count. At CAMB.AI, we're a research team of Interspeech-published, Carnegie Mellon, ex-Siri engineers and we're looking for you to join our team. |
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We're actively hiring; please drop us an email at ack@camb.ai if you're interested. Visit our [careers page](https://www.camb.ai/careers) for more info. |
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## Acknowledgements |
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Parts of code for this project are adapted from the following repositories -- please make sure to check them out! Thank you to the authors of: |
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- AWS: For providing much needed compute resources (NVIDIA H100s) to enable training of the model. |
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- TransFusion: [https://github.com/RF5/transfusion-asr](https://github.com/RF5/transfusion-asr) |
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- Multinomial diffusion: [https://github.com/ehoogeboom/multinomial_diffusion](https://github.com/ehoogeboom/multinomial_diffusion) |
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- Mistral-src: [https://github.com/mistralai/mistral-src](https://github.com/mistralai/mistral-src) |
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- minbpe: [https://github.com/karpathy/minbpe](https://github.com/karpathy/minbpe) |
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- gemelo-ai's encodec Vocos: [https://github.com/gemelo-ai/vocos](https://github.com/gemelo-ai/vocos) |
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- librosa for their `.trim()` code: [https://librosa.org/doc/main/generated/librosa.effects.trim.html](https://librosa.org/doc/main/generated/librosa.effects.trim.html) |