And lets keep it casual and funny!
Sure thing, my friend! Let’s get started with the world of Whisper Speech Recognition and Language Detection in Python. First, what is Whisper? It’s a state-of-the-art open-source speech recognition system that can transcribe audio to text with impressive accuracy. And it’s all thanks to its powerful transformer architecture and pretrained models.
Now how we use it in Python. First, you need to install the Whisper library using pip:
# This script installs the Whisper library using pip, which is a package manager for Python.
# The following line uses the "pip" command to install the "whisper" library.
# The "install" command specifies that we want to install a new package.
# "whisper" is the name of the package we want to install.
pip install whisper
Once that’s done, you can start transcribing audio files! Here’s an example script that uses Whisper to convert a WAV file into text:
# Import necessary libraries
import os # Importing the os library to access operating system functionalities
from pathlib import Path # Importing the pathlib library to work with file paths
from whisper.audio import read_wav_file # Importing the read_wav_file function from the whisper.audio module
from whisper.model_selection import select_best_model # Importing the select_best_model function from the whisper.model_selection module
from whisper.recognizer import Recognizer, Result # Importing the Recognizer and Result classes from the whisper.recognizer module
# Load the audio file and convert it to a numpy array
input_path = 'your-audio-file.wav' # Setting the input path to the audio file
output_text = '' # Initializing an empty string to store the transcribed text
with open(input_path, 'rb') as f: # Opening the audio file in read binary mode
data = read_wav_file(f) # Converting the audio file to a numpy array using the read_wav_file function
# Select the best model for your audio file based on its duration and language
model_name = select_best_model('small', 'en-us') # Selecting the best model for the audio file based on its duration and language
recognizer = Recognizer() # Initializing a Recognizer object
recognizer.load_model(model_name) # Loading the selected model into the Recognizer object
# Set up a loop to transcribe each segment of the audio file
for start, end in recognizer.recognize(data): # Looping through each segment of the audio file
result = recognizer.results[Result(start=start, end=end)] # Accessing the transcribed text for the current segment
if len(result['alternatives']) > 0: # Checking if there is at least one alternative for the transcribed text
output_text += ''.join([c for c in result['alternatives'][0] if c not in [' ', '\n', '\r']]) + '. ' # Appending the transcribed text to the output string, removing any unnecessary characters
# Print the transcribed text to the console or save it to a file
print(output_text) # Printing the transcribed text to the console
This script uses the `select_best_model()` function from Whisper’s model selection module to choose the best pretrained model for your audio file based on its duration and language. It then loads that model into memory using the `load_model()` method of the Recognizer class, which is responsible for transcribing each segment of the audio file.
The script uses a loop to iterate over each segment of the audio file and calls the `recognize()` function on it to get its transcription. It then appends that text to our output string using Python’s list comprehension syntax, which removes any whitespace or newline characters from the result.
Finally, we print the transcribed text to the console or save it to a file depending on your needs. Whisper Speech Recognition and Language Detection in Python made easy.