Update app.py

app.py

@@ -1,35 +1,47 @@
-import os
+"""
+Gradio demo for Tharshan/indicf5_hindi-english_code_switch.
+
+Tuned for HF Spaces CPU basic (2 vCPU / 16 GB):
+  - no `spaces` / ZeroGPU decorator (nothing to allocate on CPU)
+  - torch pinned to 2 threads (container over-reports core count)
+  - default 16 denoising steps instead of 32 (~halves inference time)
+  - 150-char cap, validated before any model work
+  - requests queued so each gets the full CPU instead of contending
+"""
 
 import gradio as gr
-import spaces
 import torch
 from transformers import AutoModel
 
 REPO = "Tharshan/indicf5_hindi-english_code_switch"
+MAX_CHARS = 150
 
 print("loading model ...")
 model = AutoModel.from_pretrained(REPO, trust_remote_code=True)
 model.eval()
+torch.set_num_threads(2)
 
 VOICES = model.voices()
 CHOICES = [(v.get("name", k), k) for k, v in VOICES.items()]
 DEFAULT = list(VOICES)[0]
-
-# same sentence in every language, so switching is directly comparable
-EXAMPLES = [
-    ["मैं आज office जा रहा हूँ, morning में एक important meeting है।", "ritu_hinglish"],
-    ["நான் இன்னைக்கு office போறேன், morning ல ஒரு important meeting இருக்கு.", "ta_hinglish"],
-    ["আমি আজ office যাচ্ছি, morning এ একটা important meeting আছে।", "bn"],
-    ["నేను ఈరోజు office కి వెళ్తున్నాను, morning లో ఒక important meeting ఉంది.", "te"],
-    ["ನಾನು ಇವತ್ತು office ಗೆ ಹೋಗುತ್ತಿದ್ದೇನೆ, morning ನಲ್ಲಿ ಒಂದು important meeting ಇದೆ.", "kn"],
-    ["ഞാൻ ഇന്ന് office ലേക്ക് പോകുന്നു, morning ൽ ഒരു important meeting ഉണ്ട്.", "ml"],
-    ["मी आज office ला जातोय, morning मध्ये एक important meeting आहे.", "mr"],
-    ["હું આજે office જઈ રહ્યો છું, morning માં એક important meeting છે.", "gu"],
-    ["ਮੈਂ ਅੱਜ office ਜਾ ਰਿਹਾ ਹਾਂ, morning ਵਿੱਚ ਇੱਕ important meeting ਹੈ।", "pa"],
-    ["ମୁଁ ଆଜି office ଯାଉଛି, morning ରେ ଗୋଟିଏ important meeting ଅଛି।", "or"],
+print(f"ready — voices: {list(VOICES)}")
+
+# Same sentence across languages, so switching the dropdown is directly
+# comparable. Keys must exist in voices.json or the buttons raise KeyError.
+_CANDIDATES = [
+    ("ritu_hinglish", "मैं आज office जा रहा हूँ, morning में meeting है।"),
+    ("ta_hinglish",   "நான் இன்னைக்கு office போறேன், morning ல meeting இருக்கு."),
+    ("bn",            "আমি আজ office যাচ্ছি, morning এ একটা meeting আছে।"),
+    ("te",            "నేను ఈరోజు office కి వెళ్తున్నాను, morning లో meeting ఉంది."),
+    ("kn",            "ನಾನು ಇವತ್ತು office ಗೆ ಹೋಗುತ್ತಿದ್ದೇನೆ, morning ನಲ್ಲಿ meeting ಇದೆ."),
+    ("ml",            "ഞാൻ ഇന്ന് office ലേക്ക് പോകുന്നു, morning ൽ ഒരു meeting ഉണ്ട്."),
+    ("mr",            "मी आज office ला जातोय, morning मध्ये एक meeting आहे."),
+    ("gu",            "હું આજે office જઈ રહ્યો છું, morning માં એક meeting છે."),
+    ("pa",            "ਮੈਂ ਅੱਜ office ਜਾ ਰਿਹਾ ਹਾਂ, morning ਵਿੱਚ ਇੱਕ meeting ਹੈ।"),
+    ("or",            "ମୁଁ ଆଜି office ଯାଉଛି, morning ରେ ଗୋଟିଏ meeting ଅଛି।"),
 ]
-
-MAX_CHARS = 300
+# drop any example whose voice isn't actually bundled
+EXAMPLES = [[text, key] for key, text in _CANDIDATES if key in VOICES]
 
 
 def synth(text, voice_key, nfe, speed):
@@ -37,69 +49,69 @@ def synth(text, voice_key, nfe, speed):
     if not text:
         raise gr.Error("Enter some text first.")
     if len(text) > MAX_CHARS:
-        raise gr.Error(f"Keep it under {MAX_CHARS} characters. This build "
-                       "generates in a single pass with no text chunking, so "
-                       "longer input degrades noticeably.")
+        raise gr.Error(f"Keep it under {MAX_CHARS} characters — this runs on "
+                       f"free CPU hardware. You entered {len(text)}.")
+    if voice_key not in VOICES:
+        raise gr.Error("Pick a voice from the dropdown.")
 
     ref_audio, ref_text = model.voice(voice_key)
-    model.to("cuda" if torch.cuda.is_available() else "cpu")
-
     audio, sr = model.generate(text, ref_audio=ref_audio, ref_text=ref_text,
                                nfe_step=int(nfe), speed=float(speed))
     return sr, audio
 
 
 with gr.Blocks(title="Indic code-switched TTS") as demo:
-    gr.Markdown("""
+    gr.Markdown(f"""
 # Indic code-switched TTS
 
-Text-to-speech for Indian languages with **English words mixed in** — the way
-people actually speak. Fine-tuned from
+Because nobody says "कार्यालय" when they mean office.
+
+Most Indic text-to-speech mangles English words embedded in an Indic sentence.
+This one doesn't. Fine-tuned from
 [ai4bharat/IndicF5](https://huggingface.co/ai4bharat/IndicF5) on Hindi-English
-code-switched speech.
+code-switched speech — the English gain carries across to the other scripts too.
 
-Pick a language, write a sentence with some English in it, and listen to how the
-English is pronounced inside the Indic sentence.
+> ⏳ Running on free CPU hardware: **30–60 seconds per sentence**, and the first
+> request after idle is slower while the model wakes up. Keep it short.
 """)
 
     with gr.Row():
         with gr.Column(scale=3):
             text = gr.Textbox(
-                label="Text",
-                lines=3,
-                placeholder="Write an Indic sentence with English words mixed in…",
-            )
+                label="Text", lines=2,
+                placeholder="An Indic sentence with English words mixed in…")
             voice = gr.Dropdown(choices=CHOICES, value=DEFAULT,
                                 label="Language / voice")
             with gr.Accordion("Advanced", open=False):
                 nfe = gr.Slider(8, 32, value=16, step=4,
-                                label="Denoising steps (lower = faster)")
+                                label="Denoising steps — lower is faster, "
+                                      "quality difference is small")
                 speed = gr.Slider(0.7, 1.3, value=1.0, step=0.05, label="Speed")
             btn = gr.Button("Generate", variant="primary")
 
         with gr.Column(scale=2):
             out = gr.Audio(label="Output", show_download_button=True)
-            gr.Markdown("""
-**Tip:** match the voice to your text's language. The reference voice drives
-prosody and accent, and a mismatched one degrades output substantially — more
-than any other setting here.
-""")
+            gr.Markdown(
+                "**Tip:** match the voice to your text's language. The reference "
+                "voice sets accent and prosody, and a mismatched one degrades "
+                "output more than any other setting here.")
 
-    gr.Examples(examples=EXAMPLES, inputs=[text, voice],
-                label="The same sentence in ten languages")
+    if EXAMPLES:
+        gr.Examples(examples=EXAMPLES, inputs=[text, voice],
+                    label="The same sentence across languages")
 
     gr.Markdown(f"""
 ---
-**Model:** [{REPO}](https://huggingface.co/{REPO}) ·
-fine-tuned from [ai4bharat/IndicF5](https://huggingface.co/ai4bharat/IndicF5) (MIT),
-trained on OpenSLR-104 Hindi-English.
+**Model:** [{REPO}](https://huggingface.co/{REPO}) · fine-tuned from
+[ai4bharat/IndicF5](https://huggingface.co/ai4bharat/IndicF5) (MIT) on
+OpenSLR-104 Hindi-English.
 
-**Limitations.** Single-pass generation, no text chunking — keep to a sentence or
-two. Heavy code-switching can drop words. Training audio came from an ASR corpus
-upsampled to 24 kHz, so timbre is duller than the base model.
+**Limitations.** Single-pass generation with no text chunking — one or two
+sentences at a time. Heavy code-switching can drop words. Training audio came
+from an ASR corpus upsampled to 24 kHz, so timbre is duller than the base model.
 """)
 
     btn.click(synth, inputs=[text, voice, nfe, speed], outputs=out)
     text.submit(synth, inputs=[text, voice, nfe, speed], outputs=out)
 
-demo.queue(max_size=20).launch()
+demo.queue(max_size=10).launch()