Update app.py

app.py

@@ -1,79 +1,87 @@
-import spaces
-import torch
-
-import gradio as gr
-import yt_dlp as youtube_dl
-from transformers import pipeline
+from transformers import pipeline, AutoModelForSpeechSeq2Seq, AutoProcessor
 from transformers.pipelines.audio_utils import ffmpeg_read
-
+import yt_dlp as youtube_dl
+import gradio as gr
 import tempfile
+import torch
+import time
 import os
 
-MODEL_NAME = "Kushtrim/whisper-large-v3-turbo-shqip"
-BATCH_SIZE = 8
+BATCH_SIZE = 16
 FILE_LIMIT_MB = 1000
 YT_LENGTH_LIMIT_S = 3600  # limit to 1 hour YouTube files
 
-device = 0 if torch.cuda.is_available() else "cpu"
-
-pipe = pipeline(
-    task="automatic-speech-recognition",
-    model=MODEL_NAME,
-    chunk_length_s=30,
-    device=device,
-    token=os.getenv("HF"),
-)
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+model_id = "Kushtrim/whisper-large-v3-turbo-shqip"
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, use_safetensors=True, token=True).to(device)
+processor = AutoProcessor.from_pretrained(model_id, token=True)
+pipe = pipeline("automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor,
+                chunk_length_s=30, torch_dtype=torch_dtype, device=device,
+                token=os.environ["HF"])
 
+# pipe = pipeline("automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor,
+#                 max_new_tokens=128, chunk_length_s=15, batch_size=16, torch_dtype=torch_dtype, device=device,
+#                 token=os.environ["HF"])
 
 @spaces.GPU
 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.")
+        raise gr.Error(
+            "No audio file submitted! Please upload or record an audio file before submitting your request.")
 
-    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
-    return  text
+    text = pipe(inputs, generate_kwargs={
+                "task": task, 'language': 'sq'}, return_timestamps=True)["text"]
+    return text
 
 
 def _return_yt_html_embed(yt_url):
     video_id = yt_url.split("?v=")[-1]
     HTML_str = (
-        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
+        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{
+            video_id}"> </iframe>'
         " </center>"
     )
     return HTML_str
 
+
 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"}
-    
+        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])
         except youtube_dl.utils.ExtractorError as err:
             raise gr.Error(str(err))
 
-@spaces.GPU
+
 def yt_transcribe(yt_url, task, max_filesize=75.0):
     html_embed_str = _return_yt_html_embed(yt_url)
 
@@ -84,65 +92,70 @@ def yt_transcribe(yt_url, task, max_filesize=75.0):
             inputs = f.read()
 
     inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
-    inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
+    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"]
+    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={
+                "task": task}, return_timestamps=True)["text"]
 
     return html_embed_str, text
 
 
-demo = gr.Blocks(theme=gr.themes.Default())
+demo = gr.Blocks()
 
-mf_transcribe = gr.Interface(
+file_transcribe = gr.Interface(
     fn=transcribe,
     inputs=[
-        gr.Audio(sources="microphone", type="filepath"),
-        gr.Radio(["transcribe"], label="Task", value="transcribe"),
+        gr.Audio(sources=["upload"], type="filepath", label="Audio file"),
+        gr.Radio(choices=["transcribe", "translate"], label="Task"),
     ],
     outputs="text",
     title="Whisper Large V3 Turbo Shqip: Transcribe Audio",
     description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
-    ),
+        "Easily transcribe long-form audio inputs in Albanian with high accuracy! This demo utilizes the fine-tuned "
+        f"Whisper model [{model_id}](https://huggingface.co/{model_id}), specially adapted for the Albanian language, "
+        "powered by 🤗 Transformers. With just a click, transform microphone or audio file inputs of any length into "
+        "text with exceptional transcription quality."
+    )
     allow_flagging="never",
 )
 
-file_transcribe = gr.Interface(
+mf_transcribe = gr.Interface(
     fn=transcribe,
     inputs=[
-        gr.Audio(sources="upload", type="filepath", label="Audio file"),
-        gr.Radio(["transcribe"], label="Task", value="transcribe"),
+        gr.Audio(sources=["microphone"], type="filepath"),
+        gr.Radio(choices=["transcribe", "translate"], label="Task"),
     ],
     outputs="text",
     title="Whisper Large V3 Turbo Shqip: Transcribe Audio",
     description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
-    ),
+        "Easily transcribe long-form audio inputs in Albanian with high accuracy! This demo utilizes the fine-tuned "
+        f"Whisper model [{model_id}](https://huggingface.co/{model_id}), specially adapted for the Albanian language, "
+        "powered by 🤗 Transformers. With just a click, transform microphone or audio file inputs of any length into "
+        "text with exceptional transcription quality."
+    )
     allow_flagging="never",
 )
 
 yt_transcribe = gr.Interface(
     fn=yt_transcribe,
     inputs=[
-        gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
-        gr.Radio(["transcribe"], label="Task", value="transcribe")
+        gr.Textbox(
+            lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
+        gr.Radio(choices=["transcribe", "translate"], label="Task")
     ],
     outputs=["html", "text"],
-    title="Whisper Large V3 Turbo Shqip: Transcribe YouTube",
+    title="Whisper Large V3 Turbo Shqip: Transcribe Audio",
     description=(
-        "Transcribe long-form YouTube videos with the click of a button! Demo uses the checkpoint"
-        f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of"
-        " arbitrary length."
-    ),
+        "Easily transcribe long-form audio inputs in Albanian with high accuracy! This demo utilizes the fine-tuned "
+        f"Whisper model [{model_id}](https://huggingface.co/{model_id}), specially adapted for the Albanian language, "
+        "powered by 🤗 Transformers. With just a click, transform microphone or audio file inputs of any length into "
+        "text with exceptional transcription quality."
+    )
     allow_flagging="never",
 )
 
 with demo:
-    gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"])
-
-demo.queue().launch(ssr_mode=False)
+    gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
 
+demo.launch()