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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
+
+<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** 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).
+
+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.
+
+## 📖 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
+
+### 👨‍💻 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").to(device)
+tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-v1")
+
+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 = 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").to(device)
+tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-v1")
+
+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 = 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.

config.json

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generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1025,
+  "decoder_start_token_id": 1025,
+  "min_new_tokens": 10,
+  "do_sample": true,
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+}

handler.py

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+from typing import Dict, List, Any
+from parler_tts import ParlerTTSForConditionalGeneration
+from transformers import AutoTokenizer
+import torch
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # load model and processor from path
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        self.model = ParlerTTSForConditionalGeneration.from_pretrained(path, torch_dtype=torch.float16).to("cuda")
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
+        """
+        Args:
+            data (:dict:):
+                The payload with the text prompt and generation parameters.
+        """
+        # process input
+        inputs = data.pop("inputs", data)
+        voice_description = data.pop("voice_description", "data")
+        parameters = data.pop("parameters", None)
+
+        gen_kwargs = {"min_new_tokens": 10}
+        if parameters is not None:
+            gen_kwargs.update(parameters)
+
+        # preprocess
+        inputs = self.tokenizer(
+            text=[inputs],
+            padding=True,
+            return_tensors="pt",).to("cuda")
+        voice_description = self.tokenizer(
+            text=[voice_description],
+            padding=True,
+            return_tensors="pt",).to("cuda")
+
+        # pass inputs with all kwargs in data
+        with torch.autocast("cuda"):
+            outputs = self.model.generate(**voice_description, prompt_input_ids=inputs.input_ids, **gen_kwargs)
+
+        # postprocess the prediction
+        prediction = outputs[0].cpu().numpy().tolist()
+
+        return [{"generated_audio": prediction}]

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preprocessor_config.json

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+  "padding_side": "right",
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+  "return_attention_mask": true,
+  "sampling_rate": 44100
+}

requirements.txt

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+git+https://github.com/huggingface/parler-tts.git

special_tokens_map.json

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tokenizer_config.json

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