Code for Speech Recognition in Python Tutorial

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speechrecognition.py

# importing libraries import speech_recognition as sr import os from pydub import AudioSegment from pydub.silence import split_on_silence # create a speech recognition object r = sr.Recognizer() # 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 try: 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 try: 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"])