Update app.py

app.py

@@ -1,40 +1,51 @@
 import torch
 import torchaudio
+import tempfile
+import requests
 from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
 import gradio as gr
 
 model_name = "ibm-granite/granite-speech-3.3-8b"
-
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 processor = AutoProcessor.from_pretrained(model_name)
 model = AutoModelForSpeechSeq2Seq.from_pretrained(model_name).to(device)
 
-def transcribe(audio, translate_to=None):
-    # audio: (sampling rate, numpy array) from Gradio
-    sr, audio_data = audio
-    waveform = torch.from_numpy(audio_data).unsqueeze(0)  # shape: (1, seq)
-    # Resample if not 16kHz
+def download_audio_from_url(url):
+    response = requests.get(url)
+    if response.status_code != 200:
+        raise Exception("Failed to download file from URL.")
+    tmp = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False)
+    tmp.write(response.content)
+    tmp.close()
+    waveform, sr = torchaudio.load(tmp.name)
+    return waveform, sr
+
+def transcribe_from_url(audio_url, translate_to=None):
+    waveform, sr = download_audio_from_url(audio_url)
+    # Resample if needed
     if sr != 16000:
         waveform = torchaudio.functional.resample(waveform, sr, 16000)
-    
+
     inputs = processor(waveform, sampling_rate=16000, return_tensors="pt").to(device)
-    # Set beam size >1 แนะนำ beam=5
     outputs = model.generate(**inputs, num_beams=5, max_new_tokens=512)
     text = processor.tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
 
-    # ถ้ามี translate_to, เติม prompt: e.g. "<|translate_to=es|>"
     if translate_to:
         text = f"<|translate_to={translate_to}|> " + text
         inputs2 = processor(text, return_tensors="pt").to(device)
         outputs2 = model.generate(**inputs2, num_beams=5)
         text = processor.tokenizer.batch_decode(outputs2, skip_special_tokens=True)[0]
-    
-    return text
 
-translator_options = [None, "fr", "es", "it", "de", "pt", "ja", "zh"]
+    return text
 
 gr.Interface(
-    fn=transcribe,
+    fn=transcribe_from_url,
     inputs=[
-        gr.Audio(source="microphone", type="numpy", label="Upload
+        gr.Textbox(label="🎧 Audio File URL (.mp3, .wav)", placeholder="Paste Google Drive direct link or other audio URL"),
+        gr.Dropdown(choices=[None, "fr", "es", "it", "de", "pt", "ja", "zh"], label="Translate to (optional)")
+    ],
+    outputs=gr.Textbox(label="📝 Transcription / Translation"),
+    title="Granite Speech 3.3-8B - Audio from URL",
+    description="Paste a direct URL to an audio file (Google Drive with 'uc?export=download' format or any MP3/WAV link)"
+).launch()