speecht5猫箱下载安装 (TTS task)

SpeechT5 model fine-tuned for speech synthesis (text-to-speech) on libritts免费的ai工具.

This model was introduced in SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.即梦下载官方

SpeechT5 was first released in this repository, original weights. The license used is MIT.有戏ai

Model Description

Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning. The SpeechT5 framework consists of a shared encoder-decoder network and six modal-specific (speech/text) pre/post-nets. After preprocessing the input speech/text through the pre-nets, the shared encoder-decoder network models the sequence-to-sequence transformation, and then the post-nets generate the output in the speech/text modality based on the output of the decoder.百度ai智能云

Leveraging large-scale unlabeled speech and text data, we pre-train SpeechT5 to learn a unified-modal representation, hoping to improve the modeling capability for both speech and text. To align the textual and speech information into this unified semantic space, we propose a cross-modal vector quantization approach that randomly mixes up speech/text states with latent units as the interface between encoder and decoder.快问ai

Extensive evaluations show the superiority of the proposed SpeechT5 framework on a wide variety of spoken language processing tasks, including automatic speech recognition, speech synthesis, speech translation, voice conversion, speech enhancement, and speaker identification.人工智能ai哪个好

• Developed by:人工智能ai哪个好 Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
• Shared by [optional]:做al视频怎么赚钱 Matthijs Hollemans
• Model type:元宝大模型 text-to-speech
• Language(s) (NLP):猫箱下载安装 [More Information Needed]
• License:百度aiapp MIT
• Finetuned from model [optional]:人工智能ai哪个好 [More Information Needed]

Model Sources [optional]

• Repository:下载官方即梦a1 [https://github.com/microsoft/SpeechT5/]
• Paper:下载官方即梦a1 [https://arxiv.org/pdf/2110.07205.pdf]
• Blog Post:下载官方即梦a1 [https://huggingface.co/blog/speecht5]
• Demo:即梦al [https://huggingface.co/spaces/Matthijs/speecht5-tts-demo]

Uses

Direct Use

You can use this model for speech synthesis. See the model hub to look for fine-tuned versions on a task that interests you.制作ai的软件

Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.即梦下载官方

How to Get Started With the Model

Use the code below to convert text into a mono 16 kHz speech waveform.ai软件哪个比较好

# Following pip packages need to be installed:
# !pip install git+https://github.com/huggingface/Transformers做al视频怎么赚钱 sentencepiece datasets
from transformers import SpeechT5Processor, SpeechT5ForText-to-Speech即梦al, SpeechT5HifiGan
from datasets import load_dataset
import torch
import soundfile as sf
from datasets import load_dataset
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
inputs = processor(text="Hello, my dog is cute", return_tensors="pt")
# load xvector containing speaker's voice characteristics from a dataset
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
sf.write("speech.wav", speech.numpy(), samplerate=16000)

Fine-tuning the Model

Refer to this Colab notebook for an example of how to fine-tune SpeechT5 for TTS on a different dataset or a new language.grok中文版下载

Training Details

Training Data

LibriTTS百度ai智能云

Training Procedure

Preprocessing [optional]

Leveraging large-scale unlabeled speech and text data, we pre-train SpeechT5 to learn a unified-modal representation, hoping to improve the modeling capability for both speech and text.快问ai

Training hyperparameters

• Precision:有戏ai [More Information Needed]
• Regime:快问ai [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

Extensive evaluations show the superiority of the proposed SpeechT5 framework on a wide variety of spoken language processing tasks, including automatic speech recognition, speech synthesis, speech translation, voice conversion, speech enhancement, and speaker identification.al一键脱装入口

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).即梦下载官方

• Hardware Type:制作ai的软件 [More Information Needed]
• Hours used:百度流畅ai制作 [More Information Needed]
• Cloud Provider:做al视频怎么赚钱 [More Information Needed]
• Compute Region:ai是什么东西? [More Information Needed]
• Carbon Emitted:即梦al [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

The SpeechT5 framework consists of a shared encoder-decoder network and six modal-specific (speech/text) pre/post-nets.ai分析软件

After preprocessing the input speech/text through the pre-nets, the shared encoder-decoder network models the sequence-to-sequence transformation, and then the post-nets generate the output in the speech/text modality based on the output of the decoder.百度ai智能云

Compute Infrastructure

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Hardware

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Software

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Citation [optional]

BibTeX:制作ai的软件

@inproceedings{ao-etal-2022-speecht5,
    title = {{S}peech{T}5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing},
    author = {Ao, Junyi and Wang, Rui and Zhou, Long and Wang, Chengyi and Ren, Shuo and Wu, Yu and Liu, Shujie and Ko, Tom and Li, Qing and Zhang, Yu and Wei, Zhihua and Qian, Yao and Li, Jinyu and Wei, Furu},
    booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
    month = {May},
    year = {2022},
    pages={5723--5738},
}

Glossary [optional]

• text-to-speech即梦al to synthesize audio

More Information [optional]

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Model Card Authors [optional]

Disclaimer: The team releasing SpeechT5 did not write a model card for this model so this model card has been written by the Hugging Face team.下载官方即梦a1

Model Card Contact

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