commands/python/conda/setup-conda-stt-finetune.md

metadata

description: Set up conda environment for speech-to-text fine-tuning
tags:
  - python
  - conda
  - stt
  - whisper
  - speech
  - ai
  - fine-tuning
  - project
  - gitignored

You are helping the user set up a conda environment for speech-to-text (STT) fine-tuning.

Process

1. Create base environment

   conda create -n stt-finetune python=3.11 -y
   conda activate stt-finetune
   

2. Install PyTorch with ROCm

   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.0
   

3. Install Whisper and related libraries

   pip install openai-whisper
   pip install faster-whisper  # Optimized inference
   pip install whisperx        # Advanced features
   

4. Install Hugging Face libraries

   pip install transformers
   pip install datasets
   pip install accelerate
   pip install evaluate
   pip install peft           # For LoRA fine-tuning
   

5. Install audio processing libraries

   pip install librosa         # Audio analysis
   pip install soundfile       # Audio I/O
   pip install pydub           # Audio manipulation
   pip install sox             # Audio processing
   conda install -c conda-forge ffmpeg -y  # Audio conversion
   

6. Install speech-specific tools

   pip install jiwer          # Word Error Rate calculation
   pip install speechbrain    # Speech toolkit
   pip install pyannote.audio # Speaker diarization
   

7. Install data processing tools

   pip install pandas
   pip install numpy
   pip install scipy
   pip install matplotlib
   pip install seaborn        # Visualization
   

8. Install monitoring and experimentation

   pip install wandb          # Experiment tracking
   pip install tensorboard
   

9. Install Jupyter for interactive work

   conda install -c conda-forge jupyter jupyterlab ipywidgets -y
   

10. Test installation

import torch
import whisper
import librosa
from transformers import WhisperProcessor, WhisperForConditionalGeneration

print(f"PyTorch: {torch.__version__}")
print(f"GPU available: {torch.cuda.is_available()}")
print("All libraries imported successfully!")

11. Suggest common datasets

• Common Voice (Mozilla)
• LibriSpeech
• TEDLIUM
• Custom datasets

12. Create example script

• Offer to create ~/scripts/whisper-finetune-example.py with basic setup

Output

Provide a summary showing:

• Environment name and setup status
• Installed libraries grouped by purpose
• GPU detection status
• Available VRAM for training
• Suggested datasets for fine-tuning
• Example commands for testing
• Links to documentation/tutorials