speecht5al一键脱装入口 (TTS task)

SpeechT5 model fine-tuned for speech synthesis (text-to-speech) on libritts即梦下载官方.

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.ai分析软件

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

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.ai软件哪个比较好

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.即梦al

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.制作ai的软件

• Developed by:ai是什么东西? 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]:制作ai的软件 Matthijs Hollemans
• Model type:做al视频怎么赚钱 text-to-speech
• Language(s) (NLP):即梦al [More Information Needed]
• License:ima是什么软件 MIT
• Finetuned from model [optional]:百度aiapp [More Information Needed]

Model Sources [optional]

• Repository:百度ai智能云 [https://github.com/microsoft/SpeechT5/]
• Paper:grok中文版下载 [https://arxiv.org/pdf/2110.07205.pdf]
• Blog Post:即梦al [https://huggingface.co/blog/speecht5]
• Demo:做al视频怎么赚钱 [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.ima是什么软件

Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

[More Information Needed]百度aiapp

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.下载官方即梦a1

How to Get Started With the Model

Use the code below to convert text into a mono 16 kHz speech waveform.猫箱下载安装

# Following pip packages need to be installed:
# !pip install git+https://github.com/huggingface/Transformersai是什么东西? sentencepiece datasets
from transformers import SpeechT5Processor, SpeechT5ForText-to-Speech百度aiapp, 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.ai软件哪个比较好

Training Details

Training Data

LibriTTS快问ai

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.ima是什么软件

Training hyperparameters

• Precision:ima是什么软件 [More Information Needed]
• Regime:即梦下载官方 [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.即梦al

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).快问ai

• Hardware Type:人工智能ai哪个好 [More Information Needed]
• Hours used:百度流畅ai制作 [More Information Needed]
• Cloud Provider:制作ai的软件 [More Information Needed]
• Compute Region:人工智能ai哪个好 [More Information Needed]
• Carbon Emitted:元宝大模型 [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.百度aiapp

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.下载官方即梦a1

Compute Infrastructure

[More Information Needed]做al视频怎么赚钱

Hardware

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Software

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Citation [optional]

BibTeX:grok中文版下载

@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下载官方即梦a1 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.ai是什么东西?

Model Card Contact

[More Information Needed]百度aiapp