final codebase rework

autotranscript/autotranscript.py

@@ -125,6 +125,17 @@ class AutoTranscribe:
         
         diarisation = self.diariser.diarization(dia_audio, **kwargs)
         
+        if not diarisation["segments"]:
+            warn("No segments found. Try to run transcription without diarisation.")
+            transcript = self.transcriber.transcribe(audio_file.waveform, **kwargs)
+            
+            final_transcript= {"speakers" : ["speaker01"],
+                                   "segments" : [0, len(audio_file.waveform)],
+                                   "text" : transcript}
+            
+            return Transcript(final_transcript)
+            
+        
         print("Diarisation finished. Starting transcription.")
         
         audio_file.sr = torch.Tensor([audio_file.sr]).to(audio_file.waveform.device)
@@ -140,8 +151,8 @@ class AutoTranscribe:
             
             transcript = self.transcriber.transcribe(audio, **kwargs)
             
-            final_transcript[i] = {"speaker" : diarisation["speakers"][i],
-                                   "segment" : seg,
+            final_transcript[i] = {"speakers" : diarisation["speakers"][i],
+                                   "segments" : seg,
                                    "text" : transcript}
         
         # Remove original file if needed 
@@ -233,6 +244,7 @@ def cli():
     from whisper.tokenizer import LANGUAGES , TO_LANGUAGE_CODE
     from .transcriber import WHISPER_DEFAULT_PATH
     from .diarisation import PYANNOTE_DEFAULT_PATH
+    
     def str2bool(string):
         str2val = {"True": True, "False": False}
         if string in str2val:
@@ -242,9 +254,12 @@ def cli():
 
     parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
 
-    parser.add_argument("audio_files", nargs="+", type=str,
+    parser.add_argument("-f","--audio_files", nargs="+", type=str,
                         help="List of audio files to transcribe.")
-
+    
+    parser.add_argument('--start_server', action='store_true',
+                        help='Start the Gradio app.')
+    
     parser.add_argument("--whisper_model_name", default="medium",
                         help="Name of the Whisper model to use.")
 
@@ -299,6 +314,7 @@ def cli():
     audio_files = args.audio_files
     spoken_language = args.spoken_language
     output_format = args.output_format
+    start_server = args.start_server
 
     os.makedirs(output_directory, exist_ok=True)
 
@@ -335,6 +351,10 @@ def cli():
     elif transcription_task == "wtranscribe":
         # wtranscribe code here
         pass
+    
+    if start_server:
+        from .gradio_app import gradio_app
+        gradio_app(model)
 
 if __name__ == "__main__":
     cli()

autotranscript/transcript_exporter.py

@@ -65,7 +65,7 @@ class Transcript:
             list: List of unique speaker names in the transcript.
         """
         
-        return list(set([self.transcript[id]["speaker"] for id in self.transcript]))
+        return list(set([self.transcript[id]["speakers"] for id in self.transcript]))
     
     def _extract_segments(self) -> list:
         """
@@ -75,7 +75,7 @@ class Transcript:
             list: List of segments, where each segment is represented
                     by the starting and ending times.
         """
-        return [self.transcript[id]["segment"] for id in self.transcript]
+        return [self.transcript[id]["segments"] for id in self.transcript]
 
     def __str__(self) -> str:
         """
@@ -91,11 +91,11 @@ class Transcript:
             seq = self.transcript[_id]
             
             if self.annotation:
-                speaker = self.annotation[seq["speaker"]]
+                speaker = self.annotation[seq["speakers"]]
             else:
-                speaker = seq["speaker"]
+                speaker = seq["speakers"]
             
-            segm = seq["segment"]
+            segm = seq["segments"]
             sseg = time.strftime("%H:%M:%S",time.gmtime(segm[0]))
             eseg = time.strftime("%H:%M:%S",time.gmtime(segm[1]))
             
@@ -172,7 +172,7 @@ class Transcript:
         
         for id in self.transcript:
             seq = self.transcript[id]
-            speaker = self.annotation[seq["speaker"]]
+            speaker = self.annotation[seq["speakers"]]
             fstring += f"\n\\{speaker}speaks:\n{seq['text']}"
         
         fstring += "\n\\end{drama}"

autotranscript/version.py

@@ -2,7 +2,7 @@ import os
 import subprocess as sp
 
 MAJOR = 0
-MINOR = 2
+MINOR = 1
 MICRO = 0
 MICRO_POST = 0
 ISRELEASED = False

gradio_app.py

@@ -0,0 +1,65 @@
+from autotranscript import AutoTranscribe
+import gradio as gr
+
+LANGUAGES = [
+    "Afrikaans", "Arabic", "Armenian", "Azerbaijani", "Belarusian",
+    "Bosnian", "Bulgarian", "Catalan", "Chinese", "Croatian",
+    "Czech", "Danish", "Dutch", "English", "Estonian",
+    "Finnish", "French", "Galician", "German", "Greek",
+    "Hebrew", "Hindi", "Hungarian", "Icelandic", "Indonesian",
+    "Italian", "Japanese", "Kannada", "Kazakh", "Korean",
+    "Latvian", "Lithuanian", "Macedonian", "Malay", "Marathi",
+    "Maori", "Nepali", "Norwegian", "Persian", "Polish",
+    "Portuguese", "Romanian", "Russian", "Serbian", "Slovak",
+    "Slovenian", "Spanish", "Swahili", "Swedish", "Tagalog",
+    "Tamil", "Thai", "Turkish", "Ukrainian", "Urdu",
+    "Vietnamese", "Welsh"
+]
+
+
+def gradio_server(model : AutoTranscribe):
+
+    def transcribe(audio, microphone, number_of_speakers, language):
+        kwargs = {}
+        if number_of_speakers != 0:
+            kwargs["num_speakers"] = number_of_speakers
+        if language != "None":
+            kwargs["language"] = language
+        
+        if audio is not None:
+            out = model.transcribe(audio, **kwargs)
+        elif microphone is not None:
+            out = model.transcribe(microphone , **kwargs)
+        else:
+            out = "Please upload an audio file or record one."
+        
+        
+        return str(out)
+
+    gr.Interface(
+        fn=transcribe, 
+        inputs=[
+            gr.Audio(source= "upload", type="filepath", label="Upload Your Audio File", interactive=True),
+            gr.Audio(source= "microphone", type="filepath", label="Record Your Audio", interactive=True),
+            gr.Number(value=0, label= "Number of speakers", 
+                      info = "Number of speakers in the audio file. If you don't know, leave it at 0."), 
+            # gr.Number(value=0, label= "Minimal number of speakers", 
+            #           info = "Minimal number of speakers in the audio file. If you don't know or you have specified Numspeakers, leave it at 0."),
+            gr.Dropdown(LANGUAGES,
+                        label="Languages", default="None",
+                        info="Language of the audio file. If you don't know, leave it at None.")
+        ],
+        outputs=[
+            "text"
+        ],
+        title="Audio Transcription",
+        thumbnail = "Logo_KIDA.png",
+        description="Upload an audio file to transcribe its content. Powered by AutoTranscribe!",
+        theme="soft",       # Example of a more modern theme
+    ).launch(share=True)
+    
+    
+if __name__ == "__main__":
+    
+    model = AutoTranscribe()
+    gradio_server(model)

requirements.txt

@@ -9,10 +9,6 @@ pyannote.pipeline~=2.3
 setuptools~=65.6.3
 setuptools-rust~=1.5.2
 
-torch~=1.11.0
-torchaudio~=0.11.0
-torchmetrics~=0.11.0
-torchvision~=0.12.0
 tqdm>=4.65.0
 
 #optional: 

transcribe.py

@@ -1,8 +1,38 @@
-from autotranscript.autotranscript import AutoTranscribe
+# import os
+# import sys
+# import traceback
+
+# class TracePrints(object):
+#   def __init__(self):    
+#     self.stdout = sys.stdout
+#   def write(self, s):
+#     self.stdout.write("Writing %r\n" % s)
+#     traceback.print_stack(file=self.stdout)
+
+# sys.stdout = TracePrints()
+
+# os.environ["PYANNOTE_CACHE"] = os.path.expanduser("~/PycharmProjects/autotranscript/autotranscript/models/pyannote")
+# import os
+ 
+# os.environ['TRANSFORMERS_CACHE'] = os.path.expanduser("~/PycharmProjects/autotranscript/autotranscript/models")
+# os.environ['HF_HOME'] = os.path.expanduser("~/PycharmProjects/autotranscript/autotranscript/models")
+
+
+from autotranscript import AutoTranscribe
 
 model = AutoTranscribe()
 
-text = model.transcribe("tests/test.wav")
+text = model.transcribe("test.mp4")
 
 print("Transcription:\n")
 print(text)
+
+
+# from autotranscript.misc import *
+# import os
+
+# print(os.path.exists(CACHE_DIR))
+# print(os.path.exists(WHISPER_DEFAULT_PATH))
+# print(os.path.exists(PYANNOTE_DEFAULT_PATH))
+
+# print(os.path.exists(PYANNOTE_DEFAULT_CONFIG))