| | |
| | import os |
| | import json |
| | import http.client |
| | from io import BytesIO |
| | import gradio as gr |
| | from dotenv import load_dotenv |
| | from elevenlabs.client import ElevenLabs |
| |
|
| | load_dotenv() |
| |
|
| | KEY_EL = os.getenv("ELEVENLABS_API_KEY", "") |
| | KEY_LLM = os.getenv("API_KEY_302", "") |
| |
|
| | client_el = None |
| | if KEY_EL: |
| | client_el = ElevenLabs(api_key=KEY_EL) |
| |
|
| | PROMPT_A = """\ |
| | You are a speech-language pathology helper. Review the ORIGINAL text and the ASR TRANSCRIPT |
| | (which may include recognition mistakes) and identify words or sequences that commonly provoke |
| | stuttering (e.g., complex consonant clusters, multi-syllabic constructions, challenging onsets). |
| | |
| | Provide a brief, well-organized diagnostic summary focusing on potential stutter-triggering elements. |
| | |
| | ORIGINAL: |
| | {ot} |
| | |
| | TRANSCRIPT: |
| | {tt} |
| | |
| | Do not provide advice or revision. Respond only with concise diagnostic notes and easy-to-stutter patterns. |
| | """ |
| |
|
| | PROMPT_B = """\ |
| | You are a speech-language pathology helper. Reformulate the ORIGINAL text to lower the likelihood |
| | of stuttering while keeping the original meaning, style, and intention. Favor simpler vocabulary, |
| | shorter phrases, and smoother beginnings of words. |
| | |
| | Reference diagnosis details: |
| | {ns} |
| | |
| | ORIGINAL: |
| | {ot} |
| | |
| | Your reply must contain only the fully rewritten script, no commentary. |
| | """ |
| |
|
| | PROMPT_C = """\ |
| | Transcribe both the ORIGINAL text and the ASR TRANSCRIPT into IPA, marking syllable boundaries. |
| | Return your output in a compact format with clear section labels, for example: |
| | |
| | ORIGINAL_IPA: |
| | <original in IPA with syllable breaks> |
| | |
| | TRANSCRIPT_IPA: |
| | <transcript in IPA with syllable breaks> |
| | |
| | No added explanation or extra notes. |
| | |
| | ORIGINAL: |
| | {ot} |
| | |
| | TRANSCRIPT: |
| | {tt} |
| | """ |
| |
|
| | PROMPT_D = """\ |
| | You are a speech-language pathology helper. Using the ORIGINAL script, the ASR TRANSCRIPT, |
| | and the IPA annotations, identify segments that are stutter-prone (e.g., long or complex words, |
| | difficult consonant combinations, problematic sound onsets). |
| | |
| | Provide a concise, structured diagnostic overview of likely stuttering hotspots. |
| | |
| | ORIGINAL: |
| | {ot} |
| | |
| | TRANSCRIPT: |
| | {tt} |
| | |
| | IPA_ANNOTATIONS: |
| | {ip} |
| | |
| | Do not include suggestions or edits. Only provide principled diagnostic notes. |
| | """ |
| |
|
| | def fn_transcribe(pth: str | None) -> str: |
| | if not pth: |
| | return "No audio provided. Please upload or record audio." |
| | if not KEY_EL: |
| | return "ELEVENLABS_API_KEY not set. Please configure your environment." |
| | try: |
| | with open(pth, "rb") as f: |
| | buf = BytesIO(f.read()) |
| | except Exception as e: |
| | return f"Failed to read audio: {e}" |
| | try: |
| | tr = client_el.speech_to_text.convert( |
| | file=buf, |
| | model_id="scribe_v1", |
| | tag_audio_events=True, |
| | language_code="eng", |
| | diarize=True, |
| | ) |
| | return tr.text or "" |
| | except Exception as e: |
| | return f"Transcription error: {e}" |
| |
|
| | def fn_llm(model: str, prm: str) -> str: |
| | if not KEY_LLM: |
| | return "API_KEY_302 not set. Please configure your environment." |
| | try: |
| | con = http.client.HTTPSConnection("api.302.ai") |
| | pl = json.dumps({ |
| | "model": model, |
| | "messages": [ |
| | {"role": "user", "content": prm} |
| | ] |
| | }) |
| | hd = { |
| | "Accept": "application/json", |
| | "Authorization": f"Bearer {KEY_LLM}", |
| | "Content-Type": "application/json" |
| | } |
| | con.request("POST", "/v1/chat/completions", pl, hd) |
| | rs = con.getresponse() |
| | raw = rs.read().decode("utf-8") |
| | con.close() |
| | out = json.loads(raw) |
| | msg = out.get("choices", [{}])[0].get("message", {}) |
| | tx = msg.get("content") or msg.get("text") or str(msg) |
| | return tx.strip() |
| | except Exception as e: |
| | return f"LLM API error: {e}" |
| |
|
| | def cb_transcribe(p): |
| | return gr.update(value=fn_transcribe(p)) |
| |
|
| | def cb_basic(m, o, t): |
| | prm = PROMPT_A.format(ot=o or "", tt=t or "") |
| | rz = fn_llm(m, prm) |
| | return gr.update(value=rz) |
| |
|
| | def cb_ipa(m, o, t): |
| | p1 = PROMPT_C.format(ot=o or "", tt=t or "") |
| | ipa = fn_llm(m, p1) |
| | p2 = PROMPT_D.format(ot=o or "", tt=t or "", ip=ipa or "") |
| | sm = fn_llm(m, p2) |
| | return gr.update(value=ipa), gr.update(value=sm) |
| |
|
| | def cb_rewrite(m, o, u, s): |
| | prm = PROMPT_B.format(ns=s or "", ot=o or "") |
| | rw = fn_llm(m, prm) |
| | return gr.update(value=rw) |
| |
|
| | def passthru(p): |
| | return p |
| |
|
| | with gr.Blocks(title="Stutter Up") as ui: |
| | with gr.Tabs(): |
| | with gr.Tab("Baseline"): |
| | with gr.Row(): |
| | a_r1 = gr.Audio(label="Record Audio", sources=["microphone"], type="filepath") |
| | a_f1 = gr.File(label="Audio Download", interactive=False) |
| | b_t1 = gr.Button("1) Transcribe") |
| | with gr.Row(): |
| | t_t1 = gr.Textbox(label="Transcribed Text (ASR)", interactive=False, lines=6) |
| | t_o1 = gr.Textbox(label="Original Script (input)", lines=6) |
| | dr1 = gr.Dropdown(choices=["gpt-4o-mini", "gpt-5"], value="gpt-4o-mini", label="LLM Model") |
| | b_a1 = gr.Button("2) Analyze") |
| | with gr.Row(): |
| | t_s1 = gr.Textbox(label="LLM Summary: Easy-to-Stutter Words", lines=8) |
| | t_r1 = gr.Textbox(label="Revised Script", lines=8) |
| | b_r1 = gr.Button("3) Revise Script") |
| | with gr.Row(): |
| | a_r2 = gr.Audio(label="Post-hoc Record Audio", sources=["microphone"], type="filepath") |
| | a_f2 = gr.File(label="Post-hoc Audio Download", interactive=False) |
| |
|
| | a_r1.change(fn=passthru, inputs=a_r1, outputs=a_f1) |
| | b_t1.click(fn=cb_transcribe, inputs=[a_r1], outputs=[t_t1]) |
| | b_a1.click(fn=cb_basic, inputs=[dr1, t_o1, t_t1], outputs=[t_s1]) |
| | b_r1.click(fn=cb_rewrite, inputs=[dr1, t_o1, t_t1, t_s1], outputs=[t_r1]) |
| | a_r2.change(fn=passthru, inputs=a_r2, outputs=a_f2) |
| |
|
| | with gr.Tab("Baseline+IPA"): |
| | with gr.Row(): |
| | a_r3 = gr.Audio(label="Record Audio", sources=["microphone"], type="filepath") |
| | a_f3 = gr.File(label="Audio Download", interactive=False) |
| | b_t2 = gr.Button("1) Transcribe") |
| | with gr.Row(): |
| | t_t2 = gr.Textbox(label="Transcribed Text (ASR)", interactive=False, lines=6) |
| | t_o2 = gr.Textbox(label="Original Script (input)", lines=6) |
| | t_i2 = gr.Textbox(label="IPA Annotations (LLM Output)", interactive=False, lines=6) |
| | dr2 = gr.Dropdown(choices=["gpt-4o-mini", "gpt-5"], value="gpt-4o-mini", label="LLM Model") |
| | b_a2 = gr.Button("2) Analyze (IPA → Diagnosis)") |
| | with gr.Row(): |
| | t_s2 = gr.Textbox(label="LLM Summary: Easy-to-Stutter Words (IPA-aware)", lines=8) |
| | t_r2 = gr.Textbox(label="Revised Script", lines=8) |
| | b_r2 = gr.Button("3) Revise Script") |
| | with gr.Row(): |
| | a_r4 = gr.Audio(label="Post-hoc Record Audio", sources=["microphone"], type="filepath") |
| | a_f4 = gr.File(label="Post-hoc Audio Download", interactive=False) |
| |
|
| | a_r3.change(fn=passthru, inputs=a_r3, outputs=a_f3) |
| | b_t2.click(fn=cb_transcribe, inputs=[a_r3], outputs=[t_t2]) |
| | def pipe(m, o, t): |
| | x, y = cb_ipa(m, o, t) |
| | return x, y |
| | b_a2.click(fn=pipe, inputs=[dr2, t_o2, t_t2], outputs=[t_i2, t_s2]) |
| | b_r2.click(fn=cb_rewrite, inputs=[dr2, t_o2, t_t2, t_s2], outputs=[t_r2]) |
| | a_r4.change(fn=passthru, inputs=a_r4, outputs=a_f4) |
| |
|
| | if __name__ == "__main__": |
| | ui.launch(share=False) |
| |
|
