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README.md ADDED
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+ ---
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+ library_name: transformers
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+ tags:
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+ - text-to-speech
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+ - annotation
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+ license: apache-2.0
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+ language:
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+ - en
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+ pipeline_tag: text-to-speech
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+ inference: false
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+ datasets:
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+ - parler-tts/mls_eng
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+ - parler-tts/libritts_r_filtered
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+ - parler-tts/libritts-r-filtered-speaker-descriptions
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+ - parler-tts/mls-eng-speaker-descriptions
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+ ---
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+
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+ <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'"/>
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+
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+
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+ # Parler-TTS Mini v1
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+
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+ <a target="_blank" href="https://huggingface.co/spaces/parler-tts/parler_tts">
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+ <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/>
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+ </a>
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+
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+ **Parler-TTS Mini v1** 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).
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+
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+ With [Parler-TTS Large v1](https://huggingface.co/parler-tts/parler-tts-large-v1), this is the second set of models published as part of the [Parler-TTS](https://github.com/huggingface/parler-tts) project, which aims to provide the community with TTS training resources and dataset pre-processing code.
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+
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+ ## 📖 Quick Index
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+ * [👨‍💻 Installation](#👨‍💻-installation)
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+ * [🎲 Using a random voice](#🎲-random-voice)
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+ * [🎯 Using a specific speaker](#🎯-using-a-specific-speaker)
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+ * [Motivation](#motivation)
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+ * [Optimizing inference](https://github.com/huggingface/parler-tts/blob/main/INFERENCE.md)
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+
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+ ## 🛠️ Usage
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+
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+ ### 👨‍💻 Installation
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+
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+ Using Parler-TTS is as simple as "bonjour". Simply install the library once:
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+
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+ ```sh
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+ pip install git+https://github.com/huggingface/parler-tts.git
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+ ```
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+
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+ ### 🎲 Random voice
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+
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+
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+ **Parler-TTS** has been trained to generate speech with features that can be controlled with a simple text prompt, for example:
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+
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+ ```py
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+ import torch
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+ from parler_tts import ParlerTTSForConditionalGeneration
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+ from transformers import AutoTokenizer
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+ import soundfile as sf
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+
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+ device = "cuda:0" if torch.cuda.is_available() else "cpu"
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+
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+ model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-tts-mini-v1").to(device)
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+ tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-v1")
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+
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+ prompt = "Hey, how are you doing today?"
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+ 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."
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+
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+ input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
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+ prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
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+
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+ generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
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+ audio_arr = generation.cpu().numpy().squeeze()
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+ sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)
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+ ```
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+
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+ ### 🎯 Using a specific speaker
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+
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+ 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).
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+
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+ 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.`
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+
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+ ```py
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+ import torch
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+ from parler_tts import ParlerTTSForConditionalGeneration
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+ from transformers import AutoTokenizer
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+ import soundfile as sf
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+
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+ device = "cuda:0" if torch.cuda.is_available() else "cpu"
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+
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+ model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-tts-mini-v1").to(device)
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+ tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-v1")
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+
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+ prompt = "Hey, how are you doing today?"
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+ description = "Jon's voice is monotone yet slightly fast in delivery, with a very close recording that almost has no background noise."
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+
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+ input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
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+ prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
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+
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+ generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
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+ audio_arr = generation.cpu().numpy().squeeze()
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+ sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)
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+ ```
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+
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+ **Tips**:
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+ * 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!
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+ * Include the term "very clear audio" to generate the highest quality audio, and "very noisy audio" for high levels of background noise
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+ * Punctuation can be used to control the prosody of the generations, e.g. use commas to add small breaks in speech
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+ * The remaining speech features (gender, speaking rate, pitch and reverberation) can be controlled directly through the prompt
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+
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+ ## Motivation
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+
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+ 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.
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+
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+ 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.
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+ Parler-TTS was released alongside:
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+ * [The Parler-TTS repository](https://github.com/huggingface/parler-tts) - you can train and fine-tuned your own version of the model.
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+ * [The Data-Speech repository](https://github.com/huggingface/dataspeech) - a suite of utility scripts designed to annotate speech datasets.
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+ * [The Parler-TTS organization](https://huggingface.co/parler-tts) - where you can find the annotated datasets as well as the future checkpoints.
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+
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+ ## Citation
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+
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+ If you found this repository useful, please consider citing this work and also the original Stability AI paper:
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+
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+ ```
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+ @misc{lacombe-etal-2024-parler-tts,
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+ author = {Yoach Lacombe and Vaibhav Srivastav and Sanchit Gandhi},
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+ title = {Parler-TTS},
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+ year = {2024},
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+ publisher = {GitHub},
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+ journal = {GitHub repository},
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+ howpublished = {\url{https://github.com/huggingface/parler-tts}}
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+ }
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+ ```
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+
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+ ```
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+ @misc{lyth2024natural,
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+ title={Natural language guidance of high-fidelity text-to-speech with synthetic annotations},
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+ author={Dan Lyth and Simon King},
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+ year={2024},
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+ eprint={2402.01912},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.SD}
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+ }
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+ ```
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+
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+ ## License
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+
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+ This model is permissively licensed under the Apache 2.0 license.
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+ {
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handler.py ADDED
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+ from typing import Dict, List, Any
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+ from parler_tts import ParlerTTSForConditionalGeneration
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+ from transformers import AutoTokenizer
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+ import torch
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+
6
+ class EndpointHandler:
7
+ def __init__(self, path=""):
8
+ # load model and processor from path
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+ self.tokenizer = AutoTokenizer.from_pretrained(path)
10
+ self.model = ParlerTTSForConditionalGeneration.from_pretrained(path, torch_dtype=torch.float16).to("cuda")
11
+
12
+ def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
13
+ """
14
+ Args:
15
+ data (:dict:):
16
+ The payload with the text prompt and generation parameters.
17
+ """
18
+ # process input
19
+ inputs = data.pop("inputs", data)
20
+ voice_description = data.pop("voice_description", "data")
21
+ parameters = data.pop("parameters", None)
22
+
23
+ gen_kwargs = {"min_new_tokens": 10}
24
+ if parameters is not None:
25
+ gen_kwargs.update(parameters)
26
+
27
+ # preprocess
28
+ inputs = self.tokenizer(
29
+ text=[inputs],
30
+ padding=True,
31
+ return_tensors="pt",).to("cuda")
32
+ voice_description = self.tokenizer(
33
+ text=[voice_description],
34
+ padding=True,
35
+ return_tensors="pt",).to("cuda")
36
+
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+ # pass inputs with all kwargs in data
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+ with torch.autocast("cuda"):
39
+ outputs = self.model.generate(**voice_description, prompt_input_ids=inputs.input_ids, **gen_kwargs)
40
+
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+ # postprocess the prediction
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+ prediction = outputs[0].cpu().numpy().tolist()
43
+
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+ return [{"generated_audio": prediction}]
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requirements.txt ADDED
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