Python语音计算入门：构建语音助手与微服务架构

语音助手

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"Introduction to Voice Computing in Python.pdf" 本文档是一本由Jim Schwoebel编写的关于Python中语音计算的入门指南，旨在帮助读者理解并掌握处理语音文件的各种技术，包括读/写、录制、清洗、加密、回放、转码、转录、压缩、发布、饱和化、建模和可视化等核心技能。此外，它还涵盖了从零开始构建个人语音计算机和语音助手的详细过程，使读者能够亲手创建属于自己的智能交互系统。首先，文档深入浅出地介绍了语音识别的基础知识，这涉及到Python中的音频处理库，如PyAudio和SpeechRecognition，它们分别用于录制和转录语音。通过这些库，开发者可以实现与硬件设备的交互，捕获音频输入，并将其转换为可处理的文字数据。接着，文档讨论了音频文件的处理，包括编码和解码，常见的格式如WAV、MP3等。利用librosa或pydub等库，开发者可以对音频进行剪辑、混合、调整音量等操作，满足各种应用需求。对于音频的加密和压缩，文档可能涵盖使用AES等加密算法保护数据安全，以及使用像ffmpeg这样的工具进行高效的音频压缩，以减小文件大小，便于存储和传输。此外，文档还涉及了语音合成技术，即Text-to-Speech (TTS)。Python中的gTTS（Google Text-to-Speech）和pyttsx3等库允许将文本转换为自然流畅的语音输出，这对于创建语音助手至关重要。在服务器架构方面，文档特别提到了在Docker和Kubernetes上构建前沿的微服务。Docker提供轻量级的容器化解决方案，使得语音应用程序可以轻松部署和扩展。而Kubernetes作为容器编排工具，能帮助管理和调度这些容器，确保服务的高可用性和可伸缩性。最后，文档可能会介绍机器学习和深度学习在语音识别中的应用，比如使用预训练的模型如DeepSpeech或TensorFlow的WaveNet来提升转录准确率。同时，数据可视化工具如matplotlib和seaborn可用于呈现和分析语音特征，以优化模型性能。 "Introduction to Voice Computing in Python" 是一份全面的教程，不仅教授了基本的语音处理技术，还探讨了高级话题，如微服务架构和AI在语音计算中的应用。通过学习此文档，读者不仅可以掌握语音助手的开发，还能深入了解语音计算的全貌，为未来在这个领域的工作打下坚实基础。

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I.2 The history of voice computing

Voice computing has a rich history. A quick way to think about it is in terms of seven historical

time periods: the Language period, the Music period, the Scientific period, the Analog period,

the Digital period, the Internet period, and the Voice-first period (Figure I.2.1).

Figure I.2.1

Timeline of voice computing: historical periods

Language period (300,000 BC - 2000 BC) - It all began when humans first evolved the ability

to speak and form languages around 300,000 BC.

After years of formalizing human speech

into words, humans created written languages as the first forms of sound recordings and

playback (e.g. phonemes, words, and phrases).

This information was often stored on media

such as stone tablets or papyrus. It was not until 1439 until printing presses enabled mass

distribution of books and newspapers across the world, leading to a revolution in the way

languages could be transferred across cultures and geographies.

Music period (2000 BC - 1800 AD) - Like phonemes and written languages, music was one of

the earliest forms of sound to be recorded. Ancient cultures inscribed music on cuneiform

tablets to store melodies and notes (starting in 2000 BC in modern day Iraq).

Then, in a similar

manner of how printing presses enabled the mass distribution of written languages, music

If you hate history, feel free to jump and skim through this introduction if you don’t feel like reading.

Hyoid bone - The evolution of the hyoid bone led to allowed humans to produce speech vocalizations, which later

formed the basis for language systems (e.g. through phonemes).

Language first appeared in 2690 BC with the Egyptians as hieroglyphs and morphed into many other forms (e.g.

Proto-Indo-European) until the modern day (e.g. English). Language was mostly written down by scribes manually

(e.g. monks transcribed the Bible).

Music notation – Music inscriptions inspired many iterations and forms of music theory (through Greek times all

the way up to the modern day).

boxes were invented to mass distribute melodies to households through music box disks

(1800s). Later, other inventions followed to record and playback music, such as player pianos

and player organs (1880), revolutionizing the way composers wrote and distributed music.

Scientific period (1800 - 1886) - Next came the era of scientific exploration to record analog

sounds in air and reproduce them through mechanical instruments. In 1827, the English

scientist Sir Charles Wheatstone coined the word “microphone” to hypothesize how human

sound waves could propagate and be captured in air. This led to the invention of the

phonautograph in 1857 by Édouard-Léon Scott de Martinville, which was the first device to

record sounds as they pass through air through lines etched on paper or glass.

This greatly

inspired other engineers and scientists to invent devices to capture and playback sound waves,

such as Alexander Graham Bell (e.g. 1876 - acoustic telegraph) and Thomas Edison (e.g. 1877

- phonograph). In 1880s, Ramón y Cajal made accurate drawings of the human inner ear,

showing how nerve cells propagate auditory information through electrical signals - inspiring

scientists to build electrically-driven microphones.

Analog period (1887-1970) - Scientific explorations then led to commercial innovations to

record, store, and replay sounds within analog (mechanical and electrical) mediums. The

dictaphone (1879) was the first invention that could record speech reliably; however, it was

limited to a small corporate community. The gramophone (1887) opened up the ability to record

and playback analog sound recordings that could be mass-produced through records, or

disks.

This invention inspired a wave of other innovations around analog recording devices

such as magnetic wire recording devices (1898), the liquid microphone (1903), and the

condenser microphone (1916). It was not until magnetic tape recordings (1928-1950s) that

analog recordings could be recorded for longer lengths and have relatively high quality.

Moreover, analog playback devices spawned during this period. The first headphones were

invented by Nathaniel Baldwin for the Navy in his garage in 1910. For a long time, headphones

were only used by the Navy and telephone operators; it was not until John C. Koss invented the

first pair of stereo headphones in 1958 that consumers widely adopted this form of playback

technology. Moreover, loudspeaker technology was invented in 1924 by Chester W. Rice and

Edward W. Kellogg, spawning a wave of innovation of loudspeakers in the years to come.

Around the time of the 1950s, speech recognition technology emerged and became relevant.

Early systems for recognizing speech from analog recordings were quite coarse, only being able

to recognize spoken digits (e.g. 1952 - IBM Audrey). It was not until Hidden Markov Models

(HMMs) were integrated into speech recognition systems that automated transcription software

became useful for a variety of industries (e.g. 1985 – could recognize 1,000 words via Kurzweil

Applied Intelligence).

Human ear - Edouard was inspired by the human ear and designed the phonautograph around the ear canal.

The gramophone led to a proliferation of music albums in American households and is still used today as record

players.

Speech recognition today - Now, state-of-the-art speech recognition systems have achieved roughly a 5% error

rate, which is roughly equivalent to human comprehension (e.g. 2015 - Google Speech API).

Digital period (1970-2000) - Digital sound encoding (DSE) was then invented by Sony in the

1970s, allowing for analog sound (e.g. amps) recorded through a range of types of microphones

(e.g. a condenser microphone) to be converted into digital representations (e.g. numbers).

Analog-to-digital converters (A/D converters)

and digital-to-analog converters (D/A

converters)

led to a range of compressed audio formats to emerge (e.g. .WAV file format).

These advances led to many more voice recordings to be possible across an array of consumer

hardware.

Shortly thereafter, personal computers (~1981) made voice computing accessible to

consumers. Particularly, consumers for the first time were able to hear primitive text-to-speech

systems from text typed into their keyboards (e.g. iMac - TTS license).

The advent of the

Internet (1989) allowed for voice media to be played back through web browsers (e.g.1995 -

Internet Explorer). Storage media on personal computers (PCs), like CD and DVD writers

(1990s), also greatly contributed to adoption and distribution of digital file formats. Moreover,

PCs allowed for the development of new programming languages, such as Python, which later

became quite significant for processing voice data.

Internet period (2000-2010) - The 2000s was the golden era to record and publish high-quality

digital voice recordings on the Internet.

Lossless speech codecs (e.g. 2001 - .FLAC) were

invented, significantly reducing audio file sizes without sacrificing their overall quality. The

adoption of high-fidelity microphones (e.g. MEMS mics) and sound cards on PCs led to

podcasting communities to emerge (e.g. iTunes / YouTube), allowing for voice and video

content to be distributed and monetized worldwide.

Google Chrome was also released in

2008, which created new experiences to record and playback audio within the web browser

(e.g. through embedded audio and video codecs). Smartphones (e.g. iPhones) also became

widely available, creating new opportunities to record and transmit voice content (e.g. through

VoIP networks like Skype).

Moreover, software packages like FFmpeg

and SoX

allowed for developers with personal

computers to generate and manipulate sounds from the command line. Many Python libraries

emerged (e.g. LibROSA, NLTK, scikit-learn) to featurize and model text and speech data. Taken

together, these shifts made Python a great choice for voice computing projects.

Analog-to-digital converters (A/D converters) convert analog sound (amps/sec) into samples (numbers).

Digital-to-analog converters (D/A converters) convert digital representations of sound back into analog sound.

Consumers also heard TTS systems during this time through interfaces like Atari or video games. It’s interesting

to view such consoles as voice computers, as they were one of the first interfaces for voice computing.

History of interfaces - First, we had keyboards to start and stop commands. Then, as applications became more

sophisticated, the mouse was invented to help navigate through these applications more seamlessly (e.g. save files

on computers and navigate through various applications). New interfaces then emerged as computers became

miniaturized: CD players (walkmans, etc.), ipods, and blackberries. It was not until the iPhone that the interface for

smartphones became optimal: phones became pocket computers [screen/cpu] loaded with applications enabled with

“touchscreen interfaces.” [mouse/keyboard]

iTunes - I listened to the Brain Science Podcast (hosted by Ginger Campbell) and the Stanford Entrepreneurial

Thought Leaders Seminars (hosted by Tina Seelig / Stanford) during this time.

FFmpeg is a powerful program for transducing one file format or another.

SoX is another program to manipulate audio files to do things like silence removal and automatic generation of

sounds.

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