whisper

.zshrc

@@ -8,6 +8,15 @@
 #    - conda-env: Adds support for Conda environment management
 # 4. Set the custom theme for the shell prompt
 
+# The `export QT_QPA_PLATFORM=offscreen` command is used to set the `QT_QPA_PLATFORM`
+# environment variable to `offscreen`. This is particularly useful when running Qt
+# applications in a headless environment, such as a server or a CI/CD pipeline,
+# where there is no display server available. By setting this variable, Qt
+# applications can render their graphical output offscreen, allowing them to
+# run without requiring a graphical user interface (GUI). This is commonly used for
+# automated testing, rendering, or other tasks that do not require user interaction.
+export QT_QPA_PLATFORM=offscreen
+
 # Load the custom git wrapper script
 source $HOME/toolkit/git-wrapper.zsh
 

whisper

@@ -0,0 +1,86 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+This script uses the Whisper large-v3-turbo model from OpenAI for automatic speech recognition (ASR). 
+The model is finetuned for faster performance with a minor quality trade-off. It leverages the Hugging Face 
+Transformers library to load the model and processor, and performs transcription on an input audio file.
+
+Whisper is a state-of-the-art model for ASR and speech translation, proposed in the paper "Robust Speech 
+Recognition via Large-Scale Weak Supervision" by Alec Radford et al. from OpenAI. Trained on over 5 million 
+hours of labeled data, Whisper demonstrates a strong ability to generalize to many datasets and domains in 
+a zero-shot setting.
+
+The script performs the following steps:
+1. Checks if a CUDA-enabled GPU is available and sets the appropriate device and data type.
+2. Loads the Whisper large-v3-turbo model and processor from the Hugging Face Hub.
+3. Initializes an ASR pipeline using the model and processor.
+4. Defines a function `transcribe_audio` that takes an audio file path as input, performs transcription, 
+   and outputs the result to the terminal and a text file.
+5. The script expects an audio file path as a command-line argument and calls the `transcribe_audio` function.
+
+Usage:
+    whisper <audio_file>
+
+Dependencies:
+    - torch
+    - transformers
+    - datasets
+    - accelerate
+
+Example:
+    whisper sample_audio.wav
+"""
+
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+import sys
+import os
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+model_id = "openai/whisper-large-v3-turbo"
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+)
+model.to(device)
+
+processor = AutoProcessor.from_pretrained(model_id)
+
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+def transcribe_audio(audio_path):
+    # Load audio file
+    audio = {"path": audio_path}
+    
+    # Perform transcription
+    result = pipe(audio)
+    
+    # Get the base filename and directory
+    base_filename = os.path.splitext(audio_path)[0]
+    output_text_path = base_filename + ".txt"
+    
+    # Output the result to the terminal
+    print(result["text"])
+    
+    # Save the result to a text file
+    with open(output_text_path, "w") as f:
+        f.write(result["text"])
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        print("Usage: python script.py <audio_file>")
+        sys.exit(1)
+    
+    audio_file = sys.argv[1]
+    transcribe_audio(audio_file)
+