import soundfile as sf

from txtai.pipeline import TextToSpeech

# Build pipeline
tts = TextToSpeech("NeuML/kokoro-int8-onnx")

# Generate speech
speech, rate = tts("Say something here")

# Write to file
sf.write("out.wav", speech, rate)

Usage with ONNX

This model can also be run directly with ONNX provided the input text is tokenized. Tokenization can be done with ttstokenizer. ttstokenizer is a permissively licensed library with no external dependencies (such as espeak).

Note that the txtai pipeline has additional functionality such as batching large inputs together that would need to be duplicated with this method.

python

import json
import numpy as np
import onnxruntime
import soundfile as sf

from ttstokenizer import IPATokenizer

# This example assumes the files have been downloaded locally
with open("kokoro-int8-onnx/voices.json", "r", encoding="utf-8") as f:
    voices = json.load(f)

# Create model
model = onnxruntime.InferenceSession(
    "kokoro-int8-onnx/model.onnx",
    providers=["CPUExecutionProvider"]
)

# Create tokenizer
tokenizer = IPATokenizer()

# Tokenize inputs
inputs = tokenizer("Say something here")

# Get speaker array
speaker = np.array(voices["af"], dtype=np.float32)

# Generate speech
outputs = model.run(None, {
    "tokens": [[0, *inputs, 0]],
    "style": speaker[len(inputs)],
    "speed": np.ones(1, dtype=np.float32) * 1.0
})

# Write to file
sf.write("out.wav", outputs[0], 24000)

Speaker reference

The Kokoro model has a number of built-in speakers.

When using this model, set a speaker id from the reference table below.

SPEAKER	GENDER	NATIONALITY
af	F	American
af_bella	F	American
af_nicole	F	American
af_sarah	F	American
af_sky	F	American
am_adam	M	American
af_michael	M	American
bf_emma	F	British
bf_isabella	F	British
bm_george	M	British
bm_lewis	M	British

The following shows an example on how to set a speaker id when using txtai

speech, rate = tts("Say something here", speaker="af_sky")