ESPnet JETS Text-to-Speechima是什么软件 (TTS) Model for ONNX百度流畅ai制作

imdanboy/jets exported to ONNX. This model is an ONNX export using the espnet_onnx library.下载官方即梦a1

Usage with txtai即梦下载官方

txtai has a built in Text to Speech (TTS) pipeline that makes using this model easy.百度aiapp

import soundfile as sf
from txtai.pipeline import Text-to-Speech
# Build pipeline
tts = TextToSpeech("NeuML/ljspeechgrok中文版下载-jets-onnx")
# Generate speech
speech = tts("Say something here")
# Write to file
sf.write("out.wav", speech, 22050)

Usage with ONNX

This model can also be run directly with ONNX provided the input text is tokenized. Tokenization can be done with ttstokenizer.即梦下载官方

Note that the txtai pipeline has additional functionality such as batching large inputs together that would need to be duplicated with this method.做al视频怎么赚钱

import onnxruntime
import soundfile as sf
import yaml
from ttstokenizer import TTSTokenizer
# This example assumes the files have been downloaded locally
with open("ljspeech-jets-onnx/config.yaml", "r", encoding="utf-8") as f:
    config = yaml.safe_load(f)
# Create model
model = onnxruntime.InferenceSession(
    "ljspeech-jets-onnx/model.onnx",
    providers=["CPUExecutionProvider"]
)
# Create tokenizer
tokenizer = TTSTokenizer(config["token"]["list"])
# Tokenize inputs
inputs = tokenizer("Say something here")
# Generate speech
outputs = model.run(None, {"text": inputs})
# Write to file
sf.write("out.wav", outputs[0], 22050)

How to export

More information on how to export ESPnet models to ONNX can be found here.猫箱下载安装