Create app.py

app.py

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+# app.py
+import streamlit as st
+import tempfile
+from pydub import AudioSegment
+from pyannote.audio import Pipeline
+from faster_whisper import WhisperModel
+from docx import Document
+from io import BytesIO
+
+# Streamlit設定
+st.set_page_config(page_title="話者分離付き文字起こし", layout="centered")
+st.title("🎤 話者分離付き文字起こしアプリ（Hugging Face対応）")
+
+# 音声アップロード
+uploaded_file = st.file_uploader("音声ファイルをアップロード（mp3, wav, m4a）", type=["mp3", "wav", "m4a"])
+if uploaded_file:
+    st.audio(uploaded_file)
+
+    if st.button("▶️ 文字起こしスタート"):
+        status = st.info("準備中…")
+        progress = st.progress(0)
+
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
+            sound = AudioSegment.from_file(uploaded_file)
+            sound.export(tmp.name, format="wav")
+            audio_path = tmp.name
+
+        progress.progress(20)
+        status.info("話者分離中…")
+        diarization = Pipeline.from_pretrained("pyannote/speaker-diarization")(audio_path)
+        progress.progress(50)
+
+        status.info("Whisperモデルで文字起こし中…")
+        model = WhisperModel("small", compute_type="int8")
+        segments, _ = model.transcribe(audio_path, vad_filter=True, language="ja")
+        progress.progress(70)
+
+        # 話者情報を付与
+        transcript = ""
+        word_blocks = []
+        for segment in segments:
+            start = segment.start
+            speaker = "unknown"
+            for turn in diarization.itertracks(yield_label=True):
+                if turn[0].start <= start <= turn[0].end:
+                    speaker = turn[2]
+                    break
+            line = f"[{speaker}] {segment.text.strip()}"
+            word_blocks.append((speaker, segment.text.strip()))
+            transcript += line + "\n"
+
+        progress.progress(90)
+        status.success("完了！")
+
+        # 色分け＆表示
+        st.subheader("📝 話者ごとの文字起こし結果")
+        colors = ["#E6F7FF", "#FFFAE6", "#E6FFEA", "#F9E6FF"]
+        speakers = list(sorted(set(s for s, _ in word_blocks)))
+        color_map = {s: colors[i % len(colors)] for i, s in enumerate(speakers)}
+
+        for speaker, text in word_blocks:
+            st.markdown(
+                f"<div style='background-color:{color_map[speaker]}; padding:8px; border-radius:5px; margin-bottom:4px;'>"
+                f"<b>{speaker}</b>: {text}"
+                f"</div>",
+                unsafe_allow_html=True
+            )
+
+        # Wordファイルとして出力
+        doc = Document()
+        for speaker, text in word_blocks:
+            doc.add_paragraph(f"{speaker}: {text}")
+        docx_io = BytesIO()
+        doc.save(docx_io)
+        docx_io.seek(0)
+
+        st.download_button("💾 Wordファイルでダウンロード", docx_io, file_name="transcription.docx", mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document")
+        progress.progress(100)