Update app.py

app.py

@@ -4,14 +4,10 @@ import gradio as gr
 import yt_dlp as youtube_dl
 from transformers import pipeline
 from transformers.pipelines.audio_utils import ffmpeg_read
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
-from flores200_codes import flores_codes
-from gradio.components import Audio, Dropdown, Radio, Textbox
 
 import tempfile
 import os
 
-
 MODEL_NAME = "openai/whisper-large-v2"
 BATCH_SIZE = 8
 FILE_LIMIT_MB = 1000
@@ -27,69 +23,12 @@ pipe = pipeline(
 )
 
 
-def load_models():
-    # build model and tokenizer
-    model_name_dict = {
-                'nllb-distilled-600M': 'facebook/nllb-200-distilled-600M',
-        #'nllb-distilled-1.3B': 'facebook/nllb-200-distilled-1.3B',
-                  #'nllb-1.3B': 'facebook/nllb-200-1.3B',
-                  #'nllb-distilled-1.3B': 'facebook/nllb-200-distilled-1.3B',
-                  #'nllb-3.3B': 'facebook/nllb-200-3.3B',
-                  # 'nllb-distilled-600M': 'facebook/nllb-200-distilled-600M', 
-                  }
-
-    model_dict = {}
-
-    for call_name, real_name in model_name_dict.items():
-        print('\tLoading model: %s' % call_name)
-        model = AutoModelForSeq2SeqLM.from_pretrained(real_name)
-        tokenizer = AutoTokenizer.from_pretrained(real_name)
-        model_dict[call_name+'_model'] = model
-        model_dict[call_name+'_tokenizer'] = tokenizer
-
-    return model_dict
-
-def translation(source, target, text):
-    try:
-        print("Début de la traduction")
-        if len(model_dict) == 2:
-            model_name = 'nllb-distilled-1.3B'
-            
-        start_time = time.time()
-        source = flores_codes[source]
-        target = flores_codes[target]
-        
-        model = model_dict[model_name + '_model']
-        tokenizer = model_dict[model_name + '_tokenizer']
-        
-        translator = pipeline('translation', model=model, tokenizer=tokenizer, src_lang=source, tgt_lang=target)
-        output = translator(text, max_length=400)
-        
-        end_time = time.time()
-        
-        output = output[0]['translation_text']
-        result = {'inference_time': end_time - start_time,
-                  'source': source,
-                  'target': target,
-                  'result': output}
-        print("Fin de la transcription")
-    except Exception as e:
-        print(f"Erreur lors de la transcription : {e}")     
-    return result
-
-
-def transcribe(inputs, task, source, target):
+def transcribe(inputs, task):
     if inputs is None:
         raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
 
-    try:
-        print("Début de la transcription")
-        text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
-        translated_text = translation(source, target, text)
-         print("Fin de la transcription")
-    except Exception as e:
-        print(f"Erreur lors de la transcription : {e}")
-    return  text, translated_text
+    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
+    return  text
 
 
 def _return_yt_html_embed(yt_url):
@@ -102,29 +41,29 @@ def _return_yt_html_embed(yt_url):
 
 def download_yt_audio(yt_url, filename):
     info_loader = youtube_dl.YoutubeDL()
-    
+
     try:
         info = info_loader.extract_info(yt_url, download=False)
     except youtube_dl.utils.DownloadError as err:
         raise gr.Error(str(err))
-    
+
     file_length = info["duration_string"]
     file_h_m_s = file_length.split(":")
     file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
-    
+
     if len(file_h_m_s) == 1:
         file_h_m_s.insert(0, 0)
     if len(file_h_m_s) == 2:
         file_h_m_s.insert(0, 0)
     file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
-    
+
     if file_length_s > YT_LENGTH_LIMIT_S:
         yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
         file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
         raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
-    
+
     ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
-    
+
     with youtube_dl.YoutubeDL(ydl_opts) as ydl:
         try:
             ydl.download([yt_url])
@@ -145,28 +84,19 @@ def yt_transcribe(yt_url, task, max_filesize=75.0):
     inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
 
     text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
-    translated_text = translation(source, target, text)
-
-    return html_embed_str, text, translated_text
 
-
-global model_dict
-model_dict = load_models()
+    return html_embed_str, text
 
 
 demo = gr.Blocks()
 
-lang_codes = list(flores_codes.keys())
-
 mf_transcribe = gr.Interface(
     fn=transcribe,
     inputs=[
-        Audio(source="microphone", type="filepath"),
-        Radio(["transcribe", "translate"], label="Task"),
-        Dropdown(lang_codes, default='English', label='Source'),
-        Dropdown(lang_codes, default='French', label='Target'),
+        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
+        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
     ],
-    outputs=[Textbox(label="Transcribed Text"), Textbox(label="Translated Text")],
+    outputs="text",
     layout="horizontal",
     theme="huggingface",
     title="Whisper Large V2: Transcribe Audio",
@@ -181,12 +111,10 @@ mf_transcribe = gr.Interface(
 file_transcribe = gr.Interface(
     fn=transcribe,
     inputs=[
-        Audio(source="upload", type="filepath", label="Audio file"),
-        Radio(["transcribe", "translate"], label="Task"),
-        Dropdown(lang_codes, default='English', label='Source'),
-        Dropdown(lang_codes, default='French', label='Target'),
+        gr.inputs.Audio(source="upload", type="filepath", optional=True, label="Audio file"),
+        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
     ],
-    outputs=[Textbox(label="Transcribed Text"), Textbox(label="Translated Text")],
+    outputs="text",
     layout="horizontal",
     theme="huggingface",
     title="Whisper Large V2: Transcribe Audio",
@@ -201,12 +129,10 @@ file_transcribe = gr.Interface(
 yt_transcribe = gr.Interface(
     fn=yt_transcribe,
     inputs=[
-        Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
-        Radio(["transcribe", "translate"], label="Task"),
-        Dropdown(lang_codes, default='English', label='Source'),
-        Dropdown(lang_codes, default='French', label='Target'),
+        gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
+        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe")
     ],
-    outputs=[Textbox(label="html"), Textbox(label="Transcribed Text"), Textbox(label="Translated Text")],
+    outputs=["html", "text"],
     layout="horizontal",
     theme="huggingface",
     title="Whisper Large V2: Transcribe YouTube",
@@ -221,5 +147,5 @@ yt_transcribe = gr.Interface(
 with demo:
     gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
 
-demo.launch().queue()
+demo.launch(enable_queue=True)