Update app.py

app.py

@@ -1,7 +1,5 @@
 import spaces
 import os
-# os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
-
 import gc
 from functools import partial
 import gradio as gr
@@ -10,8 +8,6 @@ from speechbrain.inference.interfaces import Pretrained, foreign_class
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 import librosa
 import whisper_timestamped as whisper
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline, Wav2Vec2ForCTC, AutoProcessor
-    
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 torch.backends.cuda.matmul.allow_tf32 = True
@@ -24,7 +20,6 @@ def clean_up_memory():
 
 @spaces.GPU(duration=15)
 def recap_sentence(string):
-    # Restore capitalization and punctuation using the model
     inputs = recap_tokenizer(["restore capitalization and punctuation: " + string], return_tensors="pt", padding=True).to(device)
     outputs = recap_model.generate(**inputs, max_length=768, num_beams=5, early_stopping=True).squeeze(0)
     recap_result = recap_tokenizer.decode(outputs, skip_special_tokens=True)
@@ -32,206 +27,85 @@ def recap_sentence(string):
 
 
 @spaces.GPU(duration=30)
-def return_prediction_w2v2(mic=None, file=None, device=device):
-    if mic is not None:
-        waveform, sr = librosa.load(mic, sr=16000)
-        waveform = waveform[:60*sr]
-        w2v2_result = w2v2_classifier.classify_file_w2v2(waveform, device)
-    elif file is not None:
-        waveform, sr = librosa.load(file, sr=16000)
-        waveform = waveform[:60*sr]
-        w2v2_result = w2v2_classifier.classify_file_w2v2(waveform, device)
-    else:
-        return "You must either provide a mic recording or a file"
-
-    recap_result = recap_sentence(w2v2_result[0])
-
-    # If the letter after punct is small, recap it
-    for i, letter in enumerate(recap_result):
-        if i > 1 and recap_result[i-2] in [".", "!", "?"] and letter.islower():
-            recap_result = recap_result[:i] + letter.upper() + recap_result[i+1:]
-
-    clean_up_memory()
-    return recap_result
-
-
-@spaces.GPU(duration=30)
-def return_prediction_whisper_mic(mic=None, device=device):
-    if mic is not None:
-        waveform, sr = librosa.load(mic, sr=16000)
-        waveform = waveform[:30*sr]
-        whisper_result = whisper_classifier.classify_file_whisper_mkd(waveform, device)
-    else:
-        return "You must provide a mic recording"
-    
-    recap_result = recap_sentence(whisper_result[0])
-
-    # If the letter after punct is small, recap it
-    for i, letter in enumerate(recap_result):
-        if i > 1 and recap_result[i-2] in [".", "!", "?"] and letter.islower():
-            recap_result = recap_result[:i] + letter.upper() + recap_result[i+1:]
-
-    clean_up_memory()
-    return recap_result
-
-
-@spaces.GPU(duration=60)
 def return_prediction_whisper_file(file=None, device=device):
-    whisper_result = []
     if file is not None:
-        waveform, sr = librosa.load(file, sr=16000)
-        waveform = waveform[:3600*sr]
+        try:
+            waveform, sr = librosa.load(file.name, sr=16000)
+        except Exception as e:
+            return f"Error loading the audio file: {str(e)}"
+        
+        waveform = waveform[:3600 * sr]
         whisper_result = whisper_classifier.classify_file_whisper_mkd_streaming(waveform, device)
     else:
-       yield "You must provide a mic recording"
-    
+        return "You must provide an audio file."
+
     recap_result = ""
     prev_segment = ""
     prev_segment_len = 0
 
-    segment_counter = 0
     for segment in whisper_result:
-        segment_counter += 1
         if prev_segment == "":
-            recap_segment= recap_sentence(segment[0])
+            recap_segment = recap_sentence(segment[0])
         else:
             prev_segment_len = len(prev_segment.split())
             recap_segment = recap_sentence(prev_segment + " " + segment[0])
-        # remove prev_segment from the beginning of the recap_result
         recap_segment = recap_segment.split()
         recap_segment = recap_segment[prev_segment_len:]
         recap_segment = " ".join(recap_segment)
         prev_segment = segment[0]
         recap_result += recap_segment + " "
 
-            # If the letter after punct is small, recap it
         for i, letter in enumerate(recap_result):
-            if i > 1 and recap_result[i-2] in [".", "!", "?"] and letter.islower():
-                recap_result = recap_result[:i] + letter.upper() + recap_result[i+1:]
-
-        yield recap_result
-
-
-def return_prediction_compare(mic=None, file=None, device=device):
-    # pipe_whisper.model.to(device)
-    # mms_model.to(device)
-    if mic is not None:
-        waveform, sr = librosa.load(mic, sr=16000)
-        waveform = waveform[:60*sr]
-        whisper_mkd_result = whisper_classifier.classify_file_whisper_mkd(waveform, device)
-        # result_generator_w2v2 = w2v2_classifier.classify_file_w2v2(mic, device)
-        whisper_result = whisper_classifier.classify_file_whisper(waveform, pipe_whisper, device)
-        mms_result_generator = whisper_classifier.classify_file_mms(waveform, processor_mms, mms_model, device)
-        
-    elif file is not None:
-        waveform, sr = librosa.load(file, sr=16000)
-        waveform = waveform[:30*sr]
-        whisper_mkd_result = whisper_classifier.classify_file_whisper_mkd(waveform, device)
-        # result_generator_w2v2 = w2v2_classifier.classify_file_w2v2(file, device)
-        whisper_result = whisper_classifier.classify_file_whisper(waveform, pipe_whisper, device)
-        mms_result_generator = whisper_classifier.classify_file_mms(waveform, processor_mms, mms_model, device)
-    else:
-        return "You must either provide a mic recording or a file"
-    # pipe_whisper.model.to("cpu")
-    # mms_model.to("cpu")
-
-    segment_results_whisper = ""
-    prev_segment_whisper = ""
-    # segment_results_w2v2 = ""
-    # prev_segment_w2v2 = ""
-    segment_results_mms = ""
-    prev_segment_mms = ""
-
-    recap_result_whisper_mkd = recap_sentence(whisper_mkd_result[0])
-    recap_result_whisper = recap_sentence(whisper_result[0])
-    recap_result_mms = recap_sentence(mms_result_generator[0])
+            if i > 1 and recap_result[i - 2] in [".", "!", "?"] and letter.islower():
+                recap_result = recap_result[:i] + letter.upper() + recap_result[i + 1:]
 
-    # If the letter after punct is small, recap it
-    for i, letter in enumerate(recap_result_whisper_mkd):
-        if i > 1 and recap_result_whisper_mkd[i-2] in [".", "!", "?"] and letter.islower():
-            recap_result_whisper_mkd = recap_result_whisper_mkd[:i] + letter.upper() + recap_result_whisper_mkd[i+1:]
-
-    for i, letter in enumerate(recap_result_whisper):
-        if i > 1 and recap_result_whisper[i-2] in [".", "!", "?"] and letter.islower():
-            recap_result_whisper = recap_result_whisper[:i] + letter.upper() + recap_result_whisper[i+1:]
-
-    for i, letter in enumerate(recap_result_mms):
-        if i > 1 and recap_result_mms[i-2] in [".", "!", "?"] and letter.islower():
-            recap_result_mms = recap_result_mms[:i] + letter.upper() + recap_result_mms[i+1:]
-    
     clean_up_memory()
-    return "Буки-Whisper:\n" + recap_result_whisper_mkd + "\n\n" + "MMS:\n" + recap_result_mms + "\n\n" + "OpenAI Whisper:\n" + recap_result_whisper
-        # yield "Our W2v2: \n" + segment_results_w2v2 + "\n\n" + "MMS transcript:\n" + segment_results_mms
-
+    return recap_result
 
-# Create a partial function with the device pre-applied
-return_prediction_whisper_mic_with_device = partial(return_prediction_whisper_mic, device=device)
-return_prediction_whisper_file_with_device = partial(return_prediction_whisper_file, device=device)
-return_prediction_w2v2_with_device = partial(return_prediction_w2v2, device=device)
 
-# Load the ASR models
+# Load the models
 whisper_classifier = foreign_class(source="Macedonian-ASR/whisper-large-v3-macedonian-asr", pymodule_file="custom_interface_app.py", classname="ASR")
 whisper_classifier = whisper_classifier.to(device)
 whisper_classifier.eval()
 
-w2v2_classifier = foreign_class(source="Macedonian-ASR/wav2vec2-aed-macedonian-asr", pymodule_file="custom_interface_app.py", classname="ASR")
-w2v2_classifier = w2v2_classifier.to(device)
-w2v2_classifier.eval()
-
-# Load the T5 tokenizer and model for restoring capitalization
 recap_model_name = "Macedonian-ASR/mt5-restore-capitalization-macedonian"
 recap_tokenizer = T5Tokenizer.from_pretrained(recap_model_name)
 recap_model = T5ForConditionalGeneration.from_pretrained(recap_model_name, torch_dtype=torch.float16)
 recap_model.to(device)
 recap_model.eval()
 
-
+# Interfaces
 mic_transcribe_whisper = gr.Interface(
-    fn=return_prediction_whisper_mic_with_device,
+    fn=return_prediction_whisper_file,
     inputs=gr.Audio(sources="microphone", type="filepath"),
     outputs=gr.Textbox(),
     allow_flagging="never",
     live=False,
 )
 
-file_transcribe_whisper = gr.Interface(
-    fn=return_prediction_whisper_file_with_device,
-    inputs=gr.Audio(sources="upload", type="filepath"),
-    outputs=gr.Textbox(),
+file_transcribe_whisper_upload = gr.Interface(
+    fn=return_prediction_whisper_file,
+    inputs=gr.File(label="Upload an Audio File", type="file"),
+    outputs=gr.Textbox(label="Transcription"),
     allow_flagging="never",
     live=True
 )
 
-mic_transcribe_w2v2 = gr.Interface(
-    fn=return_prediction_w2v2_with_device,
-    inputs=gr.Audio(sources="microphone", type="filepath"),
-    outputs=gr.Textbox(),
-    allow_flagging="never",
-    live=False,
-)
-
 project_description = '''
 <img src="https://i.ibb.co/SKDfwn9/bookie.png"
      alt="Bookie logo"
      style="float: right; width: 130px; height: 110px; margin-left: 10px;" />
-     
-## Автори:
+
+## Authors:
 1. **Дејан Порјазовски**
 2. **Илина Јакимовска**
 3. **Ордан Чукалиев**
 4. **Никола Стиков**
 
-Оваа колаборација е дел од активностите на **Центарот за напредни интердисциплинарни истражувања ([ЦеНИИс](https://ukim.edu.mk/en/centri/centar-za-napredni-interdisciplinarni-istrazhuvanja-ceniis))** при УКИМ.
-
-## Во тренирањето на овој модел се употребени податоци од:
-1. Дигитален архив за етнолошки и антрополошки ресурси ([ДАЕАР](https://iea.pmf.ukim.edu.mk/tabs/view/61f236ed7d95176b747c20566ddbda1a)) при Институтот за етнологија и антропологија, Природно-математички факултет при УКИМ.
-2. Аудио верзија на меѓународното списание [„ЕтноАнтропоЗум“](https://etno.pmf.ukim.mk/index.php/eaz/issue/archive) на Институтот за етнологија и антропологија, Природно-математички факултет при УКИМ.
-3. Аудио подкастот [„Обични луѓе“](https://obicniluge.mk/episodes/) на Илина Јакимовска
-4. Научните видеа од серијалот [„Наука за деца“](http://naukazadeca.mk), фондација [КАНТАРОТ](https://qantarot.substack.com/)
-5. Македонска верзија на [Mozilla Common Voice](https://commonvoice.mozilla.org/en/datasets) (верзија 18.0)
+This collaboration is part of the activities of the **Center for Advanced Interdisciplinary Research ([CeNIIs](https://ukim.edu.mk/en/centri/centar-za-napredni-interdisciplinarni-istrazhuvanja-ceniis))** at UKIM.
 
-## Како да придонесете за подобрување на македонските модели за препознавање на говор?
-На  следниот [линк](https://drive.google.com/file/d/1YdZJz9o1X8AMc6J4MNPnVZjASyIXnvoZ/view?usp=sharing) ќе најдете инструкции за тоа како да донирате македонски говор преку платформата Mozilla Common Voice.
+## Contribute to improving Macedonian speech recognition models
+You can find instructions for donating Macedonian speech at the following [link](https://drive.google.com/file/d/1YdZJz9o1X8AMc6J4MNPnVZjASyIXnvoZ/view?usp=sharing).
 '''
 
 # Custom CSS
@@ -248,31 +122,15 @@ css = """
 }
 """
 
-transcriber_app = gr.Blocks(css=css, delete_cache=(60, 120))
-    
+transcriber_app = gr.Blocks(css=css)
+
 with transcriber_app:
-    state = gr.State()
     gr.Markdown(project_description, elem_classes="custom-markdown")
-
-    # gr.TabbedInterface(
-    #     [mic_transcribe_whisper, mic_transcribe_compare],
-    #     ["Буки-Whisper транскрипција", "Споредба на модели"],
-    # )
-    # state = gr.State(value=[], delete_callback=lambda v: print("STATE DELETED"))
-
     gr.TabbedInterface(
-        [mic_transcribe_whisper, mic_transcribe_w2v2],
-        ["Буки-Whisper транскрипција", "Буки-Wav2vec2 транскрипција"],
+        [mic_transcribe_whisper, file_transcribe_whisper_upload],
+        ["Microphone Transcription", "Upload File for Transcription"],
     )
-    state = gr.State(value=[], delete_callback=lambda v: print("STATE DELETED"))
-
-    transcriber_app.unload(return_prediction_whisper_mic)
-    transcriber_app.unload(return_prediction_whisper_file)
-    transcriber_app.unload(return_prediction_compare)
-    transcriber_app.unload(return_prediction_w2v2)
-
 
-# transcriber_app.launch(debug=True, share=True, ssl_verify=False)
 if __name__ == "__main__":
     transcriber_app.queue()
-    transcriber_app.launch(share=True)
+    transcriber_app.launch(share=True)