Realization from Noise to High-quality Speech Synthesis: Noise Removal-Based Speech Synthesis Method

Author: Zen and the Art of Computer Programming

Realization from Noise to High-quality Speech Synthesis: Noise Removal-Based Speech Synthesis Method

Effect of Noise on Speech Synthesis

1.1. Background introduction

With the rapid development of artificial intelligence technology, speech synthesis technology has been widely used in various fields, such as smart assistants, virtual anchors, automatic driving and so on. In order to ensure the quality of speech synthesis, noise elimination technology came into being. This paper will introduce a speech synthesis method based on noise elimination, in order to provide a new idea and technical solution for the field of speech synthesis.

1.2. Purpose of the article

This paper aims to implement a speech synthesis method based on noise elimination, and elaborates its technical principle, implementation steps and optimization improvements. And through application examples and code implementation to explain, so that readers can better understand and master the technology.

1.3. Target Audience

This article is suitable for readers who are interested in speech synthesis technology, including the following groups of people:

• Practitioners in the field of speech synthesis, such as CTO, programmers, etc.;
• Scientific research workers, keep an eye on the development of algorithms and technologies;
• Scholars who need to understand the application of noise cancellation technology in speech synthesis;
• Users who have higher requirements on speech synthesis quality.

2. Technical principles and concepts

2.1. Explanation of basic concepts

Speech synthesis is a process of converting text into sound, which involves techniques such as acoustic models, language models, and noise removal. The acoustic model is responsible for simulating the generation and transmission of sound, the language model is responsible for predicting the speech corresponding to the text, and the noise removal technology is to reduce the impact of noise on speech quality during the synthesis process.

2.2. Introduction to technical principles: algorithm principles, operation steps, mathematical formulas, etc.

Currently, mainstream speech synthesis algorithms include:

• Rule-based speech synthesis methods: such as DNNT, GST, etc., suitable for the synthesis of short texts;
• Statistics-based speech synthesis methods: such as WaveNet, Transformer, etc., are suitable for the synthesis of long texts.

The main steps of the rule-based speech synthesis method are as follows:

1. Preprocessing: convert the text into a format that the model can read;
2. Decoding: convert each word in the text into a binary vector;
3. Encoding: Combining binary vectors into parameters for synthesized sounds;
4. Synthesis: Generates synthetic sounds based on parameters.

The main steps of the speech synthesis method based on statistics are as follows:

1. Preprocessing: Similar to the rule-based speech synthesis method, the text is converted into a format that the model can read;
2. Training model: use a large amount of data to train the model, learn the acoustic model and the language model;
3. Encoding: apply the trained model to the synthesis task to generate synthetic sound;
4. Adjustment parameters: Adjust the parameters of the synthesized sound according to the actual application scenario to obtain a better synthesis effect.

2.3. Comparison of related technologies

At present, rule-based speech synthesis methods have advantages in the synthesis of short texts, while statistics-based speech synthesis methods have advantages in the synthesis of long texts. However, with the continuous development of technology, the application fields of the two methods are also expanding, and each has a good performance in different scenarios.

3. Implementation steps and process

3.1. Preparatory work: environment configuration and dependency installation

First, you need to configure the Python 360 environment for the experimental environment and install the following dependencies:

python360 --no-cache-dir
pip3
 numpy
通风

3.2. Core module implementation

The core module of rule-based speech synthesis is implemented as follows:

import numpy as np
import通风
from scipy.model_sequence import Sequential
from scipy.linalg import dft
from scipy.io import save


def create_sequence(text, model_path):
    model = Sequential()
    model.add(DNNT(20, 20, model_path))
    model.add(GST())
    model.add(NoiseEliminator())
    model.add(Synth())
    model.add(Wavfile())
    model.save(model_path)
    return model


def load_sequence(model_path):
    return model.load(model_path)


def process_text(text, model_path):
    sequence = load_sequence(model_path)
    output = []
    for word in text.split():
        word_vec = np.array([ord(word) - 97, 0]) / 100
        output.append(word_vec)
    return output


def generate_audio(text, model_path, output_path):
    sequence = create_sequence(text, model_path)
    output = process_text(text, model_path)
    fft = dft(output, axis=1)
    fft = fft.real
    filename ='synthesized.wav'
    save(fft, filename, 'wav')


def main():
    text = '你好，人工智能助手！'
    model_path = './saved_models/dnnt_model.pkl'
    output_path = './ synthesized_audio/ synthesized.wav'
    generate_audio(text, model_path, output_path)


if __name__ == "__main__":
    main()

The core module of statistics-based speech synthesis is implemented as follows:

import numpy as np
import通风
from scipy.model_sequence import Sequential
from scipy.linalg import dft
from scipy.io import save


def create_sequence(text, model_path):
    model = Sequential()
    model.add(DNNT(20, 20, model_path))
    model.add(GST())
    model.add(NoiseEliminator())
    model.add(Synth())
    model.add(Wavfile())
    model.save(model_path)
    return model


def load_sequence(model_path):
    return model.load(model_path)


def process_text(text, model_path):
    sequence = load_sequence(model_path)
    output = []
    for word in text.split():
        word_vec = np.array([ord(word) - 97, 0]) / 100
        output.append(word_vec)
    return output


def generate_audio(text, model_path, output_path):
    sequence = create_sequence(text, model_path)
    output = process_text(text, model_path)
    fft = dft(output, axis=1)
    fft = fft.real
    filename ='synthesized.wav'
    save(fft, filename, 'wav')


if __name__ == "__main__":
    text = '你好，人工智能助手！'
    model_path = './saved_models/dnnt_model.pkl'
    output_path = './ synthesized_audio/ synthesized.wav'
    generate_audio(text, model_path, output_path)

2.4. Code Explanation

• create_sequence()Function: Create a rule-based speech synthesis model and return the model object;
• load_sequence()Function: load a rule-based speech synthesis model;
• process_text()Function: preprocess the incoming text and return a list containing binary vectors for each word in the text;
• generate_audio()Function: Create a rule-based speech synthesis model, use the preprocessed text for synthesis, and save the synthesized audio as a wav file.

4. Application examples and code implementation explanation

4.1. Application scenario introduction

This article demonstrates how to use a rule-based speech synthesis model to generate synthesized audio for simple text-to-speech conversion.

4.2. Application case analysis

Suppose we have a set of synthesized audio data, the data format is: audio path -> synthesis result. We can convert the synthesized audio to text using the following code:

import os

# 预处理
text = '这是一段合成的音频，请勿直接播放'
model_path = './saved_models/dnnt_model.pkl'
output_path = './ synthesized_audio/ synthesized.wav'
generate_audio(text, model_path, output_path)

# 应用实例
generate_audio('这是另一段合成的音频，请勿直接播放', model_path, output_path)

4.3. Core code implementation

import numpy as np
import os
from scipy.model_sequence import Sequential
from scipy.linalg import dft
from scipy.io import save


def create_sequence(text, model_path):
    model = Sequential()
    model.add(DNNT(20, 20, model_path))
    model.add(GST())
    model.add(NoiseEliminator())
    model.add(Synth())
    model.add(Wavfile())
    model.save(model_path)
    return model


def load_sequence(model_path):
    return model.load(model_path)


def process_text(text, model_path):
    sequence = load_sequence(model_path)
    output = []
    for word in text.split():
        word_vec = np.array([ord(word) - 97, 0]) / 100
        output.append(word_vec)
    return output


def generate_audio(text, model_path, output_path):
    sequence = create_sequence(text, model_path)
    output = process_text(text, model_path)
    fft = dft(output, axis=1)
    fft = fft.real
    filename ='synthesized.wav'
    save(fft, filename, 'wav')


if __name__ == "__main__":
    text = '这是一段合成的音频，请勿直接播放'
    model_path = './saved_models/dnnt_model.pkl'
    output_path = './ synthesized_audio/ synthesized.wav'
    generate_audio(text, model_path, output_path)

    # 合成另一段合成的音频
    text2 = '这是另一段合成的音频，请勿直接播放'
    model_path2 = './saved_models/dnnt_model.pkl'
    output_path2 = './ synthesized_audio/ synthesized.wav'
    generate_audio(text2, model_path2, output_path2)

5. Optimization and improvement

5.1. Performance optimization

The quality of synthesized audio can be improved by using deeper neural network models (e.g. DNNT, GST) as well as more complex acoustic models (e.g. WaveNet, Transformer).

5.2. Scalability Improvements

Future speech synthesis technology will pay more attention to model compression, model distillation, model snapshot, etc. to achieve better scalability.

5.3. Security Hardening

In practical applications, model security is very important. Measures should be taken to prevent unauthorized model propagation, keep sensitive data confidential, etc.

6. Conclusion and Outlook

6.1. Technical Summary

This paper introduces the speech synthesis method based on noise elimination in detail, including technical principles, implementation steps and optimization improvements. Through the synthesized audio, we can realize simple text-to-speech conversion, which provides new ideas and technical solutions for the field of speech synthesis.

6.2. Future development trends and challenges

The future speech synthesis technology will continue to develop, mainly including the following aspects:

• More advanced neural network models: such as DNNT, GST, etc.;
• More complex acoustic models: such as WaveNet, Transformer, etc.;
• Compression, model distillation, model snapshot and other technologies;
• Model Security: Prevent unauthorized model propagation, keep sensitive data confidential, etc.