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c030906 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | import os
import gradio as gr
from openai import OpenAI
from jiwer import wer
from rouge_score import rouge_scorer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
# Connect to OpenAI API
def get_client():
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
raise gr.Error("Missing OPENAI_API_KEY. Please set it in the Space Secrets.")
return OpenAI(api_key=api_key)
# Podcast-style summary prompt
SUMMARY_PROMPT = """
You are a skilled voice script writer. Convert the following lecture transcript into a speech-friendly, podcast-style script suitable for a 3–5 minute audio revision.
- Target audience is already familiar with the video and wants a clear, efficient recap.
- Preserve all key knowledge nodes and insights; do not omit or add content.
- Remove fillers, repetition, and references to slides or visuals.
- Use natural spoken language suitable for listening.
- Maintain a neutral, engaging tone.
- Format as a smooth podcast monologue.
Important Guidelines:
- The summary should be ~20% of the transcript length.
- Do not impersonate or claim to be a real professor or individual.
- Avoid mentioning specific universities, brands, or affiliations unless explicitly present.
- Do not fabricate facts, examples, or names not in the original transcript.
- Ensure all information remains faithful to the transcript.
"""
def run_pipeline(transcript_file):
if transcript_file is None:
raise gr.Error("Please upload a .txt transcript file.")
# Read transcript
with open(transcript_file.name, "r", encoding="utf-8") as f:
transcript = f.read()
client = get_client()
# Summarization
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": SUMMARY_PROMPT},
{"role": "user", "content": transcript}
]
)
script_text = response.choices[0].message.content
# TTS
audio_file_path = "summary_audio.mp3"
tts_response = client.audio.speech.create(
model="gpt-4o-mini-tts",
voice="alloy",
input=script_text
)
with open(audio_file_path, "wb") as f:
f.write(tts_response.read())
# ASR
with open(audio_file_path, "rb") as f:
asr_response = client.audio.transcriptions.create(
model="whisper-1",
file=f
)
asr_text = asr_response.text.strip()
# Evaluation
wer_score = wer(script_text.lower(), asr_text.lower())
scorer = rouge_scorer.RougeScorer(['rougeL'], use_stemmer=True)
rouge_l_score = scorer.score(transcript, asr_text)['rougeL'].fmeasure
vec = TfidfVectorizer().fit_transform([transcript, asr_text])
cos_sim = cosine_similarity(vec[0:1], vec[1:2])[0][0]
# Thresholds
pass_wer = wer_score <= 0.15
pass_rouge = rouge_l_score >= 0.20
pass_cosine = cos_sim >= 0.35
overall_pass = pass_wer and pass_rouge and pass_cosine
eval_dict = {
"WER": round(wer_score, 4),
"WER_pass": pass_wer,
"ROUGE-L_F1": round(rouge_l_score, 4),
"ROUGE_pass": pass_rouge,
"TFIDF_Cosine": round(cos_sim, 4),
"Cosine_pass": pass_cosine,
"Overall": "PASS" if overall_pass else "FAIL"
}
return script_text, audio_file_path, asr_text, eval_dict
# Build Gradio UI
with gr.Blocks() as demo:
gr.Markdown("# Transcript → Podcast Summary → TTS → ASR → Evaluation")
infile = gr.File(label="Upload Transcript (.txt)", file_types=[".txt"])
run_btn = gr.Button("Run Pipeline")
summary_out = gr.Textbox(label="Podcast-style Summary", lines=14)
audio_out = gr.Audio(label="Summary Audio", type="filepath")
asr_out = gr.Textbox(label="ASR Transcript", lines=10)
metrics_out = gr.JSON(label="Evaluation Metrics")
run_btn.click(run_pipeline, inputs=[infile],
outputs=[summary_out, audio_out, asr_out, metrics_out])
if __name__ == "__main__":
demo.launch()
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