Code for Speech Recognition in Python - Python Code

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# a function to recognize speech in the audio file # so that we don't repeat ourselves in in other functions def transcribe_audio(path): # use the audio file as the audio source with sr.AudioFile(path) as source: audio_listened = r.record(source) # try converting it to text text = r.recognize_google(audio_listened) return text # a function that splits the audio file into chunks on silence # and applies speech recognition def get_large_audio_transcription_on_silence(path): Splitting the large audio file into chunks and apply speech recognition on each of these chunks # open the audio file using pydub sound = AudioSegment.from_file(path) # split audio sound where silence is 700 miliseconds or more and get chunks chunks = split_on_silence(sound, # experiment with this value for your target audio file min_silence_len = 500, # adjust this per requirement silence_thresh = sound.dBFS-14, # keep the silence for 1 second, adjustable as well keep_silence=500, folder_name = "audio-chunks" # create a directory to store the audio chunks if not os.path.isdir(folder_name): os.mkdir(folder_name) whole_text = "" # process each chunk for i, audio_chunk in enumerate(chunks, start=1): # export audio chunk and save it in # the `folder_name` directory. chunk_filename = os.path.join(folder_name, f"chunk{i}.wav") audio_chunk.export(chunk_filename, format="wav") # recognize the chunk with sr.AudioFile(chunk_filename) as source: audio_listened = r.record(source) # try converting it to text text = r.recognize_google(audio_listened) except sr.UnknownValueError as e: print("Error:", str(e)) else: text = f"{text.capitalize()}. " print(chunk_filename, ":", text) whole_text += text # return the text for all chunks detected return whole_text # a function that splits the audio file into fixed interval chunks # and applies speech recognition def get_large_audio_transcription_fixed_interval(path, minutes=5): Splitting the large audio file into fixed interval chunks and apply speech recognition on each of these chunks # open the audio file using pydub sound = AudioSegment.from_file(path) # split the audio file into chunks chunk_length_ms = int(1000 * 60 * minutes) # convert to milliseconds chunks = [sound[i:i + chunk_length_ms] for i in range(0, len(sound), chunk_length_ms)] folder_name = "audio-fixed-chunks" # create a directory to store the audio chunks if not os.path.isdir(folder_name): os.mkdir(folder_name) whole_text = "" # process each chunk for i, audio_chunk in enumerate(chunks, start=1): # export audio chunk and save it in # the `folder_name` directory. chunk_filename = os.path.join(folder_name, f"chunk{i}.wav") audio_chunk.export(chunk_filename, format="wav") # recognize the chunk with sr.AudioFile(chunk_filename) as source: audio_listened = r.record(source) # try converting it to text text = r.recognize_google(audio_listened) except sr.UnknownValueError as e: print("Error:", str(e)) else: text = f"{text.capitalize()}. " print(chunk_filename, ":", text) whole_text += text # return the text for all chunks detected return whole_text if __name__ == "__main__": print(get_large_audio_transcription_on_silence("7601-291468-0006.wav"))

whisper_api.py

import openai
# API key
openai.api_key = "<API_KEY>"
def get_openai_api_transcription(audio_filename):
    # open the audio file
    with open(audio_filename, "rb") as audio_file:
        # transcribe the audio file
        transcription = openai.Audio.transcribe("whisper-1", audio_file) # whisper-1 is the model name
    return transcription
if __name__ == "__main__":
    transcription = get_openai_api_transcription("7601-291468-0006.wav")
    print(transcription.get("text"))

whisper_api_long.py

from pydub.silence import split_on_silence
from pydub import AudioSegment
from whisper_api import get_openai_api_transcription
import os
# a function that splits the audio file into chunks
# and applies speech recognition
def get_large_audio_transcription_on_silence(path):
    Splitting the large audio file into chunks
    and apply speech recognition on each of these chunks
    # open the audio file using pydub
    sound = AudioSegment.from_file(path)  
    # split audio sound where silence is 700 miliseconds or more and get chunks
    chunks = split_on_silence(sound,
        # experiment with this value for your target audio file
        min_silence_len = 500,
        # adjust this per requirement
        silence_thresh = sound.dBFS-14,
        # keep the silence for 1 second, adjustable as well
        keep_silence=500,
    folder_name = "audio-chunks"
    # create a directory to store the audio chunks
    if not os.path.isdir(folder_name):
        os.mkdir(folder_name)
    whole_text = ""
    # process each chunk 
    for i, audio_chunk in enumerate(chunks, start=1):
        # export audio chunk and save it in
        # the `folder_name` directory.
        chunk_filename = os.path.join(folder_name, f"chunk{i}.wav")
        audio_chunk.export(chunk_filename, format="wav")
        # recognize the chunk
        transcription = get_openai_api_transcription(chunk_filename)
        print(f"{chunk_filename}: {transcription.get('text')}")
        whole_text += " " + transcription.get("text")
    # return the text for all chunks detected
    return whole_text
# a function that splits the audio file into fixed interval chunks
# and applies speech recognition
def get_large_audio_transcription_fixed_interval(path, minutes=5):
    Splitting the large audio file into 5-minute chunks
    and apply speech recognition on each of these chunks
    # open the audio file using pydub
    sound = AudioSegment.from_file(path)  
    # split the audio file into chunks
    chunk_length_ms = int(1000 * 60 * minutes) # convert to milliseconds
    chunks = [sound[i:i + chunk_length_ms] for i in range(0, len(sound), chunk_length_ms)]
    folder_name = "audio-fixed-chunks"
    # create a directory to store the audio chunks
    if not os.path.isdir(folder_name):
        os.mkdir(folder_name)
    whole_text = ""
    # process each chunk 
    for i, audio_chunk in enumerate(chunks, start=1):
        # export audio chunk and save it in
        # the `folder_name` directory.
        chunk_filename = os.path.join(folder_name, f"chunk{i}.wav")
        audio_chunk.export(chunk_filename, format="wav")
        # recognize the chunk
        transcription = get_openai_api_transcription(chunk_filename)
        print(f"{chunk_filename}: {transcription.get('text')}")
        whole_text += " " + transcription.get("text")
    # return the text for all chunks detected
    return whole_text
if __name__ == "__main__":
    # print("\nFull text:", get_large_audio_transcription_fixed_interval("032.mp3", minutes=1))
    print("\nFull text:", get_large_audio_transcription_on_silence("7601-291468-0006.wav"))

transformers_whisper.py

from transformers import WhisperProcessor, WhisperForConditionalGeneration
import torch
import torchaudio
device = "cuda:0" if torch.cuda.is_available() else "cpu"
# whisper_model_name = "openai/whisper-tiny.en" # English-only, ~ 151 MB
# whisper_model_name = "openai/whisper-base.en" # English-only, ~ 290 MB
# whisper_model_name = "openai/whisper-small.en" # English-only, ~ 967 MB
# whisper_model_name = "openai/whisper-medium.en" # English-only, ~ 3.06 GB
whisper_model_name = "openai/whisper-tiny" # multilingual, ~ 151 MB
# whisper_model_name = "openai/whisper-base" # multilingual, ~ 290 MB
# whisper_model_name = "openai/whisper-small" # multilingual, ~ 967 MB
# whisper_model_name = "openai/whisper-medium" # multilingual, ~ 3.06 GB
# whisper_model_name = "openai/whisper-large-v2" # multilingual, ~ 6.17 GB
# load the model and the processor
whisper_processor = WhisperProcessor.from_pretrained(whisper_model_name)
whisper_model = WhisperForConditionalGeneration.from_pretrained(whisper_model_name).to(device)
def load_audio(audio_path):
  """Load the audio file & convert to 16,000 sampling rate"""
  # load our wav file
  speech, sr = torchaudio.load(audio_path)
  resampler = torchaudio.transforms.Resample(sr, 16000)
  speech = resampler(speech)
  return speech.squeeze()
def get_transcription_whisper(audio_path, model, processor, language="english", skip_special_tokens=True):
  # resample from whatever the audio sampling rate to 16000
  speech = load_audio(audio_path)
  # get the input features from the audio file
  input_features = processor(speech, return_tensors="pt", sampling_rate=16000).input_features.to(device)
  # get the forced decoder ids
  forced_decoder_ids = processor.get_decoder_prompt_ids(language=language, task="transcribe")
  # print(forced_decoder_ids)
  # generate the transcription
  predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)
  # decode the predicted ids
  transcription = processor.batch_decode(predicted_ids, skip_special_tokens=skip_special_tokens)[0]
  return transcription
if __name__ == "__main__":
    english_transcription = get_transcription_whisper("7601-291468-0006.wav",
                            whisper_model,
                            whisper_processor,
                            language="english",
                            skip_special_tokens=True)
    print("English transcription:", english_transcription)
    arabic_transcription = get_transcription_whisper("arabic-audio.wav",
                          whisper_model,
                          whisper_processor,
                          language="arabic",
                          skip_special_tokens=True)
    print("Arabic transcription:", arabic_transcription)
    spanish_transcription = get_transcription_whisper("cual-es-la-fecha-cumple.mp3",
                          whisper_model,
                          whisper_processor,
                          language="spanish",
                          skip_special_tokens=True)
    print("Spanish transcription:", spanish_transcription)
transformers_whisper_long.py
from transformers import pipeline
import torch
import torchaudio
device = "cuda:0" if torch.cuda.is_available() else "cpu"
# whisper_model_name = "openai/whisper-tiny.en" # English-only, ~ 151 MB
# whisper_model_name = "openai/whisper-base.en" # English-only, ~ 290 MB
# whisper_model_name = "openai/whisper-small.en" # English-only, ~ 967 MB
# whisper_model_name = "openai/whisper-medium.en" # English-only, ~ 3.06 GB
whisper_model_name = "openai/whisper-tiny" # multilingual, ~ 151 MB
# whisper_model_name = "openai/whisper-base" # multilingual, ~ 290 MB
# whisper_model_name = "openai/whisper-small" # multilingual, ~ 967 MB
# whisper_model_name = "openai/whisper-medium" # multilingual, ~ 3.06 GB
# whisper_model_name = "openai/whisper-large-v2" # multilingual, ~ 6.17 GB
def load_audio(audio_path):
  """Load the audio file & convert to 16,000 sampling rate"""
  # load our wav file
  speech, sr = torchaudio.load(audio_path)
  resampler = torchaudio.transforms.Resample(sr, 16000)
  speech = resampler(speech)
  return speech.squeeze()
def get_long_transcription_whisper(audio_path, pipe, return_timestamps=True, 
                                   chunk_length_s=10, stride_length_s=1):
    """Get the transcription of a long audio file using the Whisper model"""
    return pipe(load_audio(audio_path).numpy(), return_timestamps=return_timestamps,
                chunk_length_s=chunk_length_s, stride_length_s=stride_length_s)
if __name__ == "__main__":
    # initialize the pipeline
    pipe = pipeline("automatic-speech-recognition", 
                model=whisper_model_name, device=device)
    # get the transcription of a sample long audio file
    output = get_long_transcription_whisper(
        "7601-291468-0006.wav", pipe, chunk_length_s=10, stride_length_s=2)
    print(f"Transcription: {output}")
    print("="*50)
    for chunk in output["chunks"]:
        # print the timestamp and the text
        print(chunk["timestamp"], ":", chunk["text"])
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