Update app.py

app.py

@@ -102,13 +102,13 @@ async def synthesize_post(data: TextIn):
     inputs = tokenizer(text, return_tensors="pt").to(device)
     with torch.no_grad():
         output = model(**inputs)
-    if hasattr(output, "waveform"):
-        waveform = output.waveform
-    elif isinstance(output, dict) and "waveform" in output:
-        waveform = output["waveform"]
-    elif isinstance(output, (tuple, list)):
-        waveform = output[0]
-    else:
+    waveform = (
+        output.waveform if hasattr(output, "waveform") else
+        output["waveform"] if isinstance(output, dict) and "waveform" in output else
+        output[0] if isinstance(output, (tuple, list)) else
+        None
+    )
+    if waveform is None:
         return {"error": "Waveform not found in model output"}
     sample_rate = getattr(model.config, "sampling_rate", 22050)
     wav_bytes = waveform_to_wav_bytes(waveform, sample_rate=sample_rate)
@@ -116,28 +116,29 @@ async def synthesize_post(data: TextIn):
 
 @app.get("/synthesize")
 async def synthesize_get(text: str = Query(..., description="Text to synthesize"), test: bool = Query(False)):
-    if test:
-        duration_s = 2.0
-        sample_rate = 22050
-        t = np.linspace(0, duration_s, int(sample_rate * duration_s), endpoint=False)
-        freq = 440
-        waveform = 0.5 * np.sin(2 * math.pi * freq * t).astype(np.float32)
-        pcm_waveform = (waveform * 32767).astype(np.int16)
-        buf = io.BytesIO()
-        scipy.io.wavfile.write(buf, rate=sample_rate, data=pcm_waveform)
-        buf.seek(0)
-        return StreamingResponse(buf, media_type="audio/wav")
+   if test:
+    paragraphs = text.count("\n") + 1  # Tirinta paragraphs-ka qoraalka
+    duration_s = paragraphs * 6        # 6 ilbiriqsi per paragraph
+    sample_rate = 22050
+    t = np.linspace(0, duration_s, int(sample_rate * duration_s), endpoint=False)
+    freq = 440
+    waveform = 0.5 * np.sin(2 * math.pi * freq * t).astype(np.float32)
+    pcm_waveform = (waveform * 32767).astype(np.int16)
+    buf = io.BytesIO()
+    scipy.io.wavfile.write(buf, rate=sample_rate, data=pcm_waveform)
+    buf.seek(0)
+    return StreamingResponse(buf, media_type="audio/wav")
     normalized = normalize_text(text)
     inputs = tokenizer(normalized, return_tensors="pt").to(device)
     with torch.no_grad():
         output = model(**inputs)
-    if hasattr(output, "waveform"):
-        waveform = output.waveform
-    elif isinstance(output, dict) and "waveform" in output:
-        waveform = output["waveform"]
-    elif isinstance(output, (tuple, list)):
-        waveform = output[0]
-    else:
+    waveform = (
+        output.waveform if hasattr(output, "waveform") else
+        output["waveform"] if isinstance(output, dict) and "waveform" in output else
+        output[0] if isinstance(output, (tuple, list)) else
+        None
+    )
+    if waveform is None:
         return {"error": "Waveform not found in model output"}
     sample_rate = getattr(model.config, "sampling_rate", 22050)
     wav_bytes = waveform_to_wav_bytes(waveform, sample_rate=sample_rate)