Duplicate from parler-tts/parler-tts-mini-v1.1

Co-authored-by: Yoach Lacombe <ylacombe@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+library_name: transformers
+tags:
+- text-to-speech
+- annotation
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-to-speech
+inference: false
+datasets:
+- parler-tts/mls_eng
+- parler-tts/libritts_r_filtered
+- parler-tts/libritts-r-filtered-speaker-descriptions
+- parler-tts/mls-eng-speaker-descriptions
+---
+
+<img src="https://huggingface.co/datasets/parler-tts/images/resolve/main/thumbnail.png" alt="Parler Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+
+
+# Parler-TTS Mini v1.1
+
+<a target="_blank" href="https://huggingface.co/spaces/parler-tts/parler_tts">
+  <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/>
+</a>
+
+**Parler-TTS Mini v1.1** is a lightweight text-to-speech (TTS) model, trained on 45K hours of audio data, that can generate high-quality, natural sounding speech with features that can be controlled using a simple text prompt (e.g. gender, background noise, speaking rate, pitch and reverberation).
+
+🚨 **Parler-TTS Mini v1.1**  is the exact same model than [Mini v1](https://huggingface.co/parler-tts/parler-tts-mini-v1). It was trained on the same datasets and with the same training configuration. The only **change** is the use of a **better prompt tokenizer**. This tokenizer has a larger vocabulary and handles byte fallback, which simplifies multilingual training. It's based on [unsloth/llama-2-7b](https://huggingface.co/unsloth/llama-2-7b) tokenizer. Thanks to the **[AI4Bharat](https://ai4bharat.iitm.ac.in/) team** who provided advice and assistance in improving tokenization. 🚨
+
+
+## 📖 Quick Index
+* [👨‍💻 Installation](#👨‍💻-installation)
+* [🎲 Using a random voice](#🎲-random-voice)
+* [🎯 Using a specific speaker](#🎯-using-a-specific-speaker)
+* [Motivation](#motivation)
+* [Optimizing inference](https://github.com/huggingface/parler-tts/blob/main/INFERENCE.md)
+
+## 🛠️ Usage
+
+🚨Unlike previous versions of Parler-TTS, here we use two tokenizers - one for the prompt and one for the description.🚨
+
+### 👨‍💻 Installation
+
+Using Parler-TTS is as simple as "bonjour". Simply install the library once:
+
+```sh
+pip install git+https://github.com/huggingface/parler-tts.git
+```
+
+### 🎲 Random voice
+
+
+**Parler-TTS** has been trained to generate speech with features that can be controlled with a simple text prompt, for example:
+
+```py
+import torch
+from parler_tts import ParlerTTSForConditionalGeneration
+from transformers import AutoTokenizer
+import soundfile as sf
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-tts-mini-v1.1").to(device)
+tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-v1.1")
+description_tokenizer = AutoTokenizer.from_pretrained(model.config.text_encoder._name_or_path)
+
+prompt = "Hey, how are you doing today?"
+description = "A female speaker delivers a slightly expressive and animated speech with a moderate speed and pitch. The recording is of very high quality, with the speaker's voice sounding clear and very close up."
+
+input_ids = description_tokenizer(description, return_tensors="pt").input_ids.to(device)
+prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+
+generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
+audio_arr = generation.cpu().numpy().squeeze()
+sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)
+```
+
+### 🎯 Using a specific speaker
+
+To ensure speaker consistency across generations, this checkpoint was also trained on 34 speakers, characterized by name (e.g. Jon, Lea, Gary, Jenna, Mike, Laura).
+
+To take advantage of this, simply adapt your text description to specify which speaker to use: `Jon's voice is monotone yet slightly fast in delivery, with a very close recording that almost has no background noise.`
+
+```py
+import torch
+from parler_tts import ParlerTTSForConditionalGeneration
+from transformers import AutoTokenizer
+import soundfile as sf
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-tts-mini-v1.1").to(device)
+tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-v1.1")
+description_tokenizer = AutoTokenizer.from_pretrained(model.config.text_encoder._name_or_path)
+
+prompt = "Hey, how are you doing today?"
+description = "Jon's voice is monotone yet slightly fast in delivery, with a very close recording that almost has no background noise."
+
+input_ids = description_tokenizer(description, return_tensors="pt").input_ids.to(device)
+prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+
+generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
+audio_arr = generation.cpu().numpy().squeeze()
+sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)
+```
+
+**Tips**:
+* We've set up an [inference guide](https://github.com/huggingface/parler-tts/blob/main/INFERENCE.md) to make generation faster. Think SDPA, torch.compile, batching and streaming!
+* Include the term "very clear audio" to generate the highest quality audio, and "very noisy audio" for high levels of background noise
+* Punctuation can be used to control the prosody of the generations, e.g. use commas to add small breaks in speech
+* The remaining speech features (gender, speaking rate, pitch and reverberation) can be controlled directly through the prompt
+
+## Motivation
+
+Parler-TTS is a reproduction of work from the paper [Natural language guidance of high-fidelity text-to-speech with synthetic annotations](https://www.text-description-to-speech.com) by Dan Lyth and Simon King, from Stability AI and Edinburgh University respectively. 
+
+Contrarily to other TTS models, Parler-TTS is a **fully open-source** release. All of the datasets, pre-processing, training code and weights are released publicly under permissive license, enabling the community to build on our work and develop their own powerful TTS models.
+Parler-TTS was released alongside:
+* [The Parler-TTS repository](https://github.com/huggingface/parler-tts) - you can train and fine-tuned your own version of the model.
+* [The Data-Speech repository](https://github.com/huggingface/dataspeech) - a suite of utility scripts designed to annotate speech datasets.
+* [The Parler-TTS organization](https://huggingface.co/parler-tts) - where you can find the annotated datasets as well as the future checkpoints.
+
+## Citation
+
+If you found this repository useful, please consider citing this work and also the original Stability AI paper:
+
+```
+@misc{lacombe-etal-2024-parler-tts,
+  author = {Yoach Lacombe and Vaibhav Srivastav and Sanchit Gandhi},
+  title = {Parler-TTS},
+  year = {2024},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/huggingface/parler-tts}}
+}
+```
+
+```
+@misc{lyth2024natural,
+      title={Natural language guidance of high-fidelity text-to-speech with synthetic annotations},
+      author={Dan Lyth and Simon King},
+      year={2024},
+      eprint={2402.01912},
+      archivePrefix={arXiv},
+      primaryClass={cs.SD}
+}
+```
+
+## License
+
+This model is permissively licensed under the Apache 2.0 license.

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