made cli work with new interface

scraibe/app/app_starter.py

@@ -0,0 +1,28 @@
+"""
+This script is used to start the Gradio interface for audio transcription.
+A configuration file can be passed to the script to configure the interface.
+If no configuration file is passed, the default configuration is used.
+The main Reason for this script is to allow the use of multiprocessing in the app.
+"""
+
+import multiprocessing
+from scraibe.misc import ParseKwargs
+from argparse import ArgumentParser
+
+parser = ArgumentParser()
+
+parser.add_argument("--server-config", type=str, default= None,
+                        help="Path to the configy.yml file.")
+    
+parser.add_argument('--server-kwargs', nargs='*', action=ParseKwargs, default={},
+                    help='Keyword arguments for the Gradio app.')
+
+args = parser.parse_args()
+
+if __name__ == '__main__':
+    
+    multiprocessing.set_start_method('spawn')
+
+    from scraibe.app.app import app
+    
+    app(config = args.server_config, **args.server_kwargs)

scraibe/cli.py

@@ -5,10 +5,11 @@ The function includes arguments for specifying the audio files, model paths,
 output formats, and other options necessary for transcription.
 """
 import os 
-from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
+from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter, Action
 import json
 
 from .autotranscript import Scraibe
+from .misc import ParseKwargs
 from .app.app import gradio_Interface
 
 from whisper.tokenizer import LANGUAGES , TO_LANGUAGE_CODE
@@ -41,13 +42,15 @@ def cli():
                         help="List of audio files to transcribe.")
     
     group.add_argument('--start-server', action='store_true',
-                        help='Start the Gradio app.')
+                        help='Start the Gradio app.' \
+                        'If set, all other arguments are ignored' \
+                        'besides --server-config or --server-kwargs.')
     
-    parser.add_argument("--port", type=int, default= None,
+    parser.add_argument("--server-config", type=str, default= None,
-                        help="Port to run the Gradio app on. Defaults to 7860.")
+                        help="Path to the configy.yml file.")
     
-    parser.add_argument("--server-name", type=str, default= None,
+    parser.add_argument('--server-kwargs', nargs='*', action=ParseKwargs, default={},
-                        help="Name of the Gradio app. If empty 127.0.0.1 or 0.0.0.0 will be used.")
+                        help='Keyword arguments for the Gradio app.')
                         
     parser.add_argument("--whisper-model-name", default="medium",
                         help="Name of the Whisper model to use.")
@@ -66,7 +69,8 @@ def cli():
                         help="Device to use for PyTorch inference.")
 
     parser.add_argument("--num-threads", type=int, default=0,
-                        help="Number of threads used by torch for CPU inference; overrides MKL_NUM_THREADS/OMP_NUM_THREADS.")
+                        help="Number of threads used by torch for CPU inference; '\
+                            'overrides MKL_NUM_THREADS/OMP_NUM_THREADS.")
 
     parser.add_argument("--output-directory", "-o", type=str, default=".",
                         help="Directory to save the transcription outputs.")
@@ -113,55 +117,59 @@ def cli():
     if arg_dict["whisper_model_directory"]:
         class_kwargs["download_root"] = arg_dict.pop("whisper_model_directory")
 
-    model = Scraibe(**class_kwargs)
+    if not start_server:
+        
+        model = Scraibe(**class_kwargs)
+
+        if arg_dict["audio_files"]:
+            audio_files = arg_dict.pop("audio_files")
+            
+            if task == "autotranscribe" or task == "autotranscribe+translate":
+                for audio in audio_files:
+                    if task == "autotranscribe+translate":
+                        task = "translate"
+                    else:
+                        task = "transcribe"
+                        
+                    out = model.autotranscribe(audio,task = task, language=arg_dict.pop("language"), verbose = arg_dict.pop("verbose_output"))
+                    basename = audio.split("/")[-1].split(".")[0]
+                    print(f'Saving {basename}.{out_format} to {out_folder}')
+                    out.save(os.path.join(out_folder, f"{basename}.{out_format}"))
+                    
+            elif task == "diarization":
+                for audio in audio_files:
+                    if arg_dict.pop("verbose_output"):
+                        print(f"Verbose not implemented for diarization.")
+                        
+                    out = model.diarization(audio)
+                    basename = audio.split("/")[-1].split(".")[0]
+                    path = os.path.join(out_folder, f"{basename}.{out_format}")
+                    
+                    print(f'Saving {basename}.{out_format} to {out_folder}')
+                    
+                    with open(path, "w") as f:
+                        json.dump(json.dumps(out, indent= 1), f)
+
+            elif task == "transcribe" or task == "translate":
+                
+                for audio in audio_files:
+    
+                    out = model.transcribe(audio, task = task,
+                                        language= arg_dict.pop("language"),
+                                        verbose = arg_dict.pop("verbose_output"))
+                    basename = audio.split("/")[-1].split(".")[0]
+                    path = os.path.join(out_folder, f"{basename}.{out_format}")
+                    with open(path, "w") as f:
+                        f.write(out)  
                     
     
-    if arg_dict["audio_files"]:
+    else: # unfinished code
-        audio_files = arg_dict.pop("audio_files")
+        import subprocess
+        import sys
         
-        if task == "autotranscribe" or task == "autotranscribe+translate":
+        execute_path = os.path.join(os.path.dirname(__file__), "app/app_starter.py")
-            for audio in audio_files:
-                if task == "autotranscribe+translate":
-                    task = "translate"
-                else:
-                    task = "transcribe"
-                    
-                out = model.autotranscribe(audio,task = task, language=arg_dict.pop("language"), verbose = arg_dict.pop("verbose_output"))
-                basename = audio.split("/")[-1].split(".")[0]
-                print(f'Saving {basename}.{out_format} to {out_folder}')
-                out.save(os.path.join(out_folder, f"{basename}.{out_format}"))
-                
-        elif task == "diarization":
-            for audio in audio_files:
-                if arg_dict.pop("verbose_output"):
-                    print(f"Verbose not implemented for diarization.")
-                    
-                out = model.diarization(audio)
-                basename = audio.split("/")[-1].split(".")[0]
-                path = os.path.join(out_folder, f"{basename}.{out_format}")
-                
-                print(f'Saving {basename}.{out_format} to {out_folder}')
-                
-                with open(path, "w") as f:
-                    json.dump(json.dumps(out, indent= 1), f)
-
-        elif task == "transcribe" or task == "translate":
-            
-            for audio in audio_files:
- 
-                out = model.transcribe(audio, task = task,
-                                       language= arg_dict.pop("language"),
-                                       verbose = arg_dict.pop("verbose_output"))
-                basename = audio.split("/")[-1].split(".")[0]
-                path = os.path.join(out_folder, f"{basename}.{out_format}")
-                with open(path, "w") as f:
-                    f.write(out)  
-                
-    
-    if start_server: # unfinished code
-        
-        gradio_Interface(model).queue().launch(server_port=args.port, server_name=args.server_name)
         
+        subprocess.run([sys.executable, execute_path])
 
 if __name__ == "__main__":
     cli()

scraibe/misc.py

@@ -1,6 +1,7 @@
 import os
 import yaml
 from pyannote.audio.core.model import CACHE_DIR as PYANNOTE_CACHE_DIR
+from argparse import Action
 
 CACHE_DIR = os.getenv(
     "AUTOT_CACHE",
@@ -38,3 +39,17 @@ def config_diarization_yaml(file_path: str, path_to_segmentation: str = None) ->
 
     with open(file_path, "w") as stream:
         yaml.dump(yml, stream)
+
+class ParseKwargs(Action):
+    """
+    Custom argparse action to parse keyword arguments.
+    """
+    def __call__(self, parser, namespace, values, option_string=None):
+        setattr(namespace, self.dest, dict())
+        for value in values:
+            key, value = value.split('=')
+            try:
+                value = eval(value)
+            except:
+                pass
+            getattr(namespace, self.dest)[key] = value