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README.md

@@ -1,13 +1,18 @@
 ---
-title: S1 Mini Transcript Cleanup
-emoji: 💻
-colorFrom: blue
-colorTo: pink
+title: S1-mini Transcript Cleanup
+emoji: ✍️
+colorFrom: green
+colorTo: red
 sdk: gradio
 sdk_version: 6.24.0
-python_version: '3.12'
 app_file: app.py
-pinned: false
+short_description: Clean and normalize raw ASR transcripts with S1-mini
+python_version: "3.12"
+startup_duration_timeout: 30m
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+ASR transcript cleanup demo using [superwhisper/s1-mini](https://huggingface.co/superwhisper/s1-mini),
+a 0.6B Qwen3-based text normalizer. Paste raw speech-to-text output and get cleaned
+text with proper punctuation, truecasing, filler removal, and formatting.
+
+The model is by Superwhisper and is licensed under Apache 2.0 with a naming clause.

app.py

@@ -0,0 +1,140 @@
+import spaces  # MUST come before torch / transformers
+import torch
+import gradio as gr
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MODEL_ID = "superwhisper/s1-mini"
+
+SYSTEM_PROMPT = (
+    "You are a text normalizer for speech-to-text transcripts. "
+    "The input begins with a control line specifying the styling, structure, "
+    "and context settings; clean the transcript to match those settings "
+    "and output only the cleaned text."
+)
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.bfloat16,
+    attn_implementation="sdpa",
+).to("cuda")
+model.eval()
+
+
+@spaces.GPU(duration=30)
+def clean_transcript(
+    transcript: str,
+    styling: str = "semi-formal",
+    structure: str = "prose",
+    context: str = "general",
+) -> str:
+    """Clean and normalize a raw ASR transcript.
+
+    Applies punctuation, truecasing, filler removal, and formatting based on
+    the selected styling, structure, and context settings.
+
+    Args:
+        transcript: Raw ASR transcript text (disfluent, unpunctuated).
+        styling: Register / formality level (casual, semi-casual, semi-formal, formal).
+        structure: Output structure (prose or lists).
+        context: Context mode (general or email).
+
+    Returns:
+        Cleaned, normalized transcript as plain text.
+    """
+    control = f"[Styling: {styling}] [Structure: {structure}] [Context: {context}]"
+    messages = [
+        {"role": "system", "content": SYSTEM_PROMPT},
+        {"role": "user", "content": f"{control}\n{transcript}"},
+    ]
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True,
+        enable_thinking=False,
+    )
+    inputs = tokenizer(text, return_tensors="pt").to(model.device)
+    input_len = inputs.input_ids.shape[1]
+    max_new = min(1024, int(input_len * 1.3) + 32)
+    with torch.no_grad():
+        out = model.generate(
+            **inputs,
+            max_new_tokens=max_new,
+            do_sample=False,
+        )
+    generated = out[0][input_len:]
+    result = tokenizer.decode(generated, skip_special_tokens=True).strip()
+    return result
+
+
+CSS = """
+#col-container { max-width: 900px; margin: 0 auto; }
+.dark .gradio-container { color: var(--body-text-color); }
+"""
+
+with gr.Blocks(theme=gr.themes.Citrus(), css=CSS) as demo:
+    gr.Markdown(
+        "# S1-mini · ASR Transcript Cleanup\n"
+        "A 0.6B Qwen3-based model that cleans raw speech-to-text transcripts: "
+        "removes fillers, resolves self-corrections, adds punctuation and truecasing, "
+        "and formats numbers/dates/emails — all controlled by styling and structure settings.\n\n"
+        "Model: [superwhisper/s1-mini](https://huggingface.co/superwhisper/s1-mini)"
+    )
+
+    with gr.Column(elem_id="col-container"):
+        with gr.Row():
+            transcript_input = gr.Textbox(
+                label="Raw ASR Transcript",
+                placeholder="Paste raw, unpunctuated speech-to-text output here…",
+                lines=6,
+                scale=4,
+            )
+        with gr.Row():
+            styling = gr.Dropdown(
+                choices=["casual", "semi-casual", "semi-formal", "formal"],
+                value="semi-formal",
+                label="Styling",
+                scale=1,
+            )
+            structure = gr.Dropdown(
+                choices=["prose", "lists"],
+                value="prose",
+                label="Structure",
+                scale=1,
+            )
+            context = gr.Dropdown(
+                choices=["general", "email"],
+                value="general",
+                label="Context",
+                scale=1,
+            )
+        run_btn = gr.Button("Clean Transcript", variant="primary")
+        output = gr.Textbox(
+            label="Cleaned Transcript",
+            lines=6,
+            show_copy_button=True,
+        )
+
+    run_btn.click(
+        fn=clean_transcript,
+        inputs=[transcript_input, styling, structure, context],
+        outputs=output,
+        api_name="clean",
+    )
+
+    gr.Examples(
+        examples=[
+            ["so um i need to like send the the report by uh friday no wait make that thursday"],
+            ["hey can you like um check the the numbers for q3 and also um make sure the the spreadsheet is up to date"],
+            ["hi john um i was wondering if you could um send me the the quarterly report by end of day friday thanks"],
+            ["so the meeting is at um three pm on tuesday and we need to like bring the the slides and also the budget numbers"],
+        ],
+        inputs=transcript_input,
+        outputs=output,
+        fn=clean_transcript,
+        cache_examples=True,
+        cache_mode="lazy",
+    )
+
+demo.launch(mcp_server=True)

requirements.txt

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+transformers>=4.51.0
+torch
+accelerate