Fix python version

requirements.txt

@@ -1,2 +1,2 @@
-whisper;python_version=="3.9"
-gradio===3.27.0;python_version=="3.9"
+whisper;python_version=="3.10"
+gradio===3.27.0;python_version=="3.10"

script.py

@@ -1,87 +0,0 @@
-# Basic script for using the OpenAI Whisper model to transcribe a video file. You can uncomment whichever model you want to use.
-# Author: ThioJoe ( https://github.com/ThioJoe )
-
-# Required third party packages: whisper
-# See instructions for setup here: https://github.com/openai/whisper#setup
-#   - You can use the below command to pull the repo and install dependencies, then just put this script in the repo directory:
-#     pip install git+https://github.com/openai/whisper.git
-
-import whisper
-import io
-import time
-import os
-import json
-import pathlib
-
-# Choose model to use by uncommenting
-# modelName = "tiny.en"
-modelName = "base.en"
-# modelName = "small.en"
-# modelName = "medium.en"
-# modelName = "large-v2"
-
-# Other Variables
-# (bool) Whether to export the segment data to a json file. Will include word level timestamps if word_timestamps is True.
-exportTimestampData = True
-outputFolder = "Output"
-
-#  ----- Select variables for transcribe method  -----
-# audio: path to audio file
-verbose = True  # (bool): Whether to display the text being decoded to the console. If True, displays all the details, If False, displays minimal details. If None, does not display anything
-language = "english"  # Language of audio file
-# (bool): Extract word-level timestamps using the cross-attention pattern and dynamic time warping, and include the timestamps for each word in each segment.
-word_timestamps = False
-# initial_prompt="" # (optional str): Optional text to provide as a prompt for the first window. This can be used to provide, or "prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns to make it more likely to predict those word correctly.
-
-#  -------------------------------------------------------------------------
-print(f"Using Model: {modelName}")
-filePath = input("Path to File Being Transcribed: ")
-filePath = filePath.strip("\"")
-if not os.path.exists(filePath):
-    print("Problem Getting File...")
-    input("Press Enter to Exit...")
-    exit()
-
-# If output folder does not exist, create it
-if not os.path.exists(outputFolder):
-    os.makedirs(outputFolder)
-    print("Created Output Folder.\n")
-
-# Get filename stem using pathlib (filename without extension)
-fileNameStem = pathlib.Path(filePath).stem
-
-resultFileName = f"{fileNameStem}.txt"
-jsonFileName = f"{fileNameStem}.json"
-
-model = whisper.load_model(modelName)
-start = time.time()
-
-#  ---------------------------------------------------
-result = model.transcribe(audio=filePath, language=language,
-                          word_timestamps=word_timestamps, verbose=verbose)
-#  ---------------------------------------------------
-
-end = time.time()
-elapsed = float(end - start)
-
-# Save transcription text to file
-print("\nWriting transcription to file...")
-with open(os.path.join(outputFolder, resultFileName), "w", encoding="utf-8") as file:
-    file.write(result["text"])
-print("Finished writing transcription file.")
-
-# Sav
-# e the segments data to json file
-# if word_timestamps == True:
-if exportTimestampData == True:
-    print("\nWriting segment data to file...")
-    with open(os.path.join(outputFolder, jsonFileName), "w", encoding="utf-8") as file:
-        segmentsData = result["segments"]
-        json.dump(segmentsData, file, indent=4)
-    print("Finished writing segment data file.")
-
-elapsedMinutes = str(round(elapsed/60, 2))
-print(f"\nElapsed Time With {modelName} Model: {elapsedMinutes} Minutes")
-
-input("Press Enter to exit...")
-exit()