Python Speech Recognition Tutorial

Python Speech Recognition Tutorial – Full Course for Beginners

Tutorial Summary:

• Introduces listening to podcast APIs and summarizing their content.
• Shows how to integrate features for chapterization and summary generation using Assembly AI.

Objective of this tutorial:

• The main goal of this tutorial is to educate learners on practical implementations of speech recognition, audio processing, and natural language understanding using Python.
• It aims to provide hands-on experience through project-based learning, encouraging users to build real applications.

Structure of this tutorial:

• Introduction:
• Introduces the course concept and instructors.
• Overview of Assembly AI and its speech-to-text API.
• Audio Processing Basics:
• Discusses audio file formats (MP3, FLAC, WAV).
• Covers key audio parameters (number of channels, sample width, frame rate).
• Demonstrates loading, saving, and plotting WAV files using the wave module.
• Recording and Saving Audio:
• Introduces pyaudio for microphone input.
• Records audio and saves it as a WAV file.
• Comments on loading other audio formats like MP3.
• Speech Recognition:
• Describes using Assembly AI's API for speech-to-text conversion.
• Steps for obtaining API keys and uploading audio for transcription.
• Discusses polling for transcription results.
• Sentiment Analysis:
• Shows how to perform sentiment analysis on YouTube video reviews.
• Discusses the integration of Assembly AI API to analyze textual sentiment.
• Building a Voice Assistant:
• Explains how to implement a real-time speech recognition system.
• Guides on creating a chatbot using OpenAI's API.
• Includes concepts of WebSockets and asynchronous programming in Python.

What you will learn from this tutorial: 

• Audio Concepts:
• Understanding different audio formats and parameters is crucial for effective audio manipulation.
• Assembly AI API:
• Familiarity with the API is essential for capturing audio and converting it to text.
• Project Diversity:
• The course covers a wide range of applications, from simple audio recordings to complex implementations like voice assistants and summarization tools.
• Tools and Libraries:
• Key libraries introduced include pyaudio, wave, and requests for handling audio and making API calls.
• Real-world Applications:
• Emphasizes real-world applications of speech recognition and natural language processing, such as sentiment analysis for products and media, summarization tasks, and assistant technologies.

Step-by-Step Tutorial: Implementing Speech Recognition in Python

1. Introduction to the Course

• Objective: Understand the basics of speech recognition and natural language processing.
• Overview of Assembly AI: A company providing a speech-to-text API.

---

2. Audio Processing Basics

A. Understanding Audio File Formats

1. Familiarize: Learn about different audio formats:
   • MP3: Lossy compression format.
   • FLAC: Lossless compression format.
   • WAV: Uncompressed format, ideal for high-quality audio.

B. Key Audio Parameters

1. Channels: Mono (1) or Stereo (2).
2. Sample Width: Indicates the number of bytes per audio sample.
3. Frame Rate: Number of samples per second (e.g., 44,100 Hz for CD Quality).
4. Frames: Total number of audio frames.

C. Loading and Plotting WAV Files

1. Use the wave Module: Learn to open and manipulate WAV files.
2. Plotting: Install and import matplotlib and numpy to visualize audio signals.

python

Copy

import wave
import numpy as np
import matplotlib.pyplot as plt

---

3. Recording and Saving Audio

A. Setup

1. Install PyAudio: Use pip install pyaudio for audio recording.
2. Setup Parameters:
   • Set frame rate, format, and channel details.

B. Recording Audio

1. Create a stream and record audio data from the microphone.
2. Save the recording as a WAV file using the wave module.

python

Copy

import pyaudio   

# Setup parameters
CHUNK = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 16000

# Record audio
p = pyaudio.PyAudio()
stream = p.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK)
frames = []

for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
    data = stream.read(CHUNK)
    frames.append(data)

stream.stop_stream()
stream.close()
p.terminate()

# Save to WAV
wf = wave.open('output.wav', 'wb')
wf.setnchannels(CHANNELS)
wf.setsampwidth(p.get_sample_size(FORMAT))
wf.setframerate(RATE)
wf.writeframes(b''.join(frames))
wf.close()

---

4. Speech Recognition Using Assembly AI

A. Register for an API Key

• Sign up at Assembly AI to get your API keys for authentication.

B. Upload Audio File for Transcription

1. Using Requests Library:

python

Copy

import requests

# Replace with your API key
API_KEY = 'your_api_key_here'
headers = {'authorization': API_KEY}

# Upload audio
with open('output.wav', 'rb') as f:
    response = requests.post('https://api.assemblyai.com/v2/upload', headers=headers, data=f)
upload_url = response.json()['upload_url']

C. Start Transcription

python

Copy

transcript_request = {
    "audio_url": upload_url
}
transcript_response = requests.post('https://api.assemblyai.com/v2/transcript', headers=headers, json=transcript_request)
transcript_id = transcript_response.json()['id']

D. Poll for Results

python

Copy

polling_url = f"https://api.assemblyai.com/v2/transcript/{transcript_id}"

while True:
    polling_response = requests.get(polling_url, headers=headers)
    status = polling_response.json()['status']
    if status == 'completed':
        print(polling_response.json()['text'])
        break
    elif status == 'failed':
        print("Transcription failed.")
        break

---

5. Sentiment Analysis on YouTube Video Reviews

A. Use YouTube DL Package

1. Install the package: pip install youtube-dl.
2. Extract text from YouTube videos for sentiment analysis.