docs: document Docker image usage for WhisperX in README

- Add a new section to the README describing how to use pre-built Docker images for WhisperX with example commands.
- Provide a link to the Docker image repository for available tags.

Signed-off-by: CHEN, CHUN <jim60105@gmail.com>

# Conflicts:
#	README.md

README.md

@@ -97,6 +97,18 @@ uv sync --all-extras --dev
 
 You may also need to install ffmpeg, rust etc. Follow openAI instructions here https://github.com/openai/whisper#setup.
 
+### 3. Docker Images
+
+Execute pre-built WhisperX container images:
+
+```bash
+docker run --gpus all -it -v ".:/app" ghcr.io/jim60105/whisperx:base-en     -- --output_format srt audio.mp3
+docker run --gpus all -it -v ".:/app" ghcr.io/jim60105/whisperx:large-v3-ja -- --output_format srt audio.mp3
+docker run --gpus all -it -v ".:/app" ghcr.io/jim60105/whisperx:no_model    -- --model tiny --language en --output_format srt audio.mp3
+```
+
+Review the tag lists in this repository: [jim60105/docker-whisperX](https://github.com/jim60105/docker-whisperX)
+
 ### Common Issues & Troubleshooting 🔧
 
 #### libcudnn Dependencies (GPU Users)
@@ -189,7 +201,7 @@ result = model.transcribe(audio, batch_size=batch_size)
 print(result["segments"]) # before alignment
 
 # delete model if low on GPU resources
-# import gc; gc.collect(); torch.cuda.empty_cache(); del model
+# import gc; import torch; gc.collect(); torch.cuda.empty_cache(); del model
 
 # 2. Align whisper output
 model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
@@ -198,7 +210,7 @@ result = whisperx.align(result["segments"], model_a, metadata, audio, device, re
 print(result["segments"]) # after alignment
 
 # delete model if low on GPU resources
-# import gc; gc.collect(); torch.cuda.empty_cache(); del model_a
+# import gc; import torch; gc.collect(); torch.cuda.empty_cache(); del model_a
 
 # 3. Assign speaker labels
 diarize_model = whisperx.diarize.DiarizationPipeline(use_auth_token=YOUR_HF_TOKEN, device=device)