chore: import upstream snapshot with attribution

2026-07-13 12:37:47 +08:00
commit 7653f56fed
1422 changed files with 359026 additions and 0 deletions
@@ -0,0 +1,109 @@
+from mcp.server.fastmcp import FastMCP
+import assemblyai as aai
+import os
+from typing import Any, Dict, List
+from dotenv import load_dotenv
+
+load_dotenv()
+aai.settings.api_key = os.getenv("ASSEMBLYAI_API_KEY")
+
+mcp = FastMCP("AssemblyAI Audio Analysis")
+
+def _format_timestamp(ms: int) -> str:
+    # Convert milliseconds to HH:MM:SS
+    seconds_total = ms // 1000
+    hours = seconds_total // 3600
+    minutes = (seconds_total % 3600) // 60
+    seconds = seconds_total % 60
+    return f"{hours:02}:{minutes:02}:{seconds:02}"
+
+@mcp.tool()
+def transcribe_audio(audio_location: str) -> dict:
+    """
+    This MCP tool accepts either a URL or an absolute local path to an audio file, transcribes it
+    and returns a summary of the transcript.
+
+    Args:
+        audio_location: The full absolute path or URL to the audio file to transcribe.
+
+    Returns:
+        A summary of the transcript.
+    """
+    config = aai.TranscriptionConfig(
+        speaker_labels=True,
+        iab_categories=True,
+        speakers_expected=2,
+        sentiment_analysis=True,
+        summarization=True,
+        language_detection=True
+    )
+    global transcript
+    transcript = aai.Transcriber().transcribe(audio_location, config=config)
+    return transcript.summary
+
+@mcp.tool()
+def get_audio_data(
+    text: bool = False,
+    timestamps: bool = False,
+    summary: bool = False,
+    speakers: bool = False,
+    sentiment: bool = False,
+    topics: bool = False
+) -> dict:
+    """
+    This MCP tool accepts a set of flags and returns a dictionary of features from the last transcript.
+
+    Args:
+        text: full transcript text
+        timestamps: timestamped sentences
+        summary: summary
+        speakers: speaker labels
+        sentiment: sentiment analysis
+        topics: topic categories
+
+    Returns:
+        A dictionary of features from the last transcript.
+    """
+
+    if transcript is None:
+        return {"error": "No transcript available. Please run transcribe_audio first."}
+
+    out: Dict[str, Any] = {}
+    if text:
+        out["text"] = " ".join(s.text for s in transcript.get_sentences())
+    if timestamps:
+        out["sentences"] = [
+            {"timestamp": _format_timestamp(s.start), "text": s.text}
+            for s in transcript.get_sentences()
+        ]
+    if summary:
+        out["summary"] = transcript.summary
+    if speakers:
+        out["speakers"] = [
+            {
+                "speaker": u.speaker,
+                "timestamp": _format_timestamp(u.start),
+                "text": u.text
+            }
+            for u in transcript.utterances
+        ]
+    if sentiment:
+        sl = transcript.sentiment_analysis
+        counts = {"POSITIVE": 0, "NEUTRAL": 0, "NEGATIVE": 0}
+        details = []
+        for s in sl:
+            counts[s.sentiment] += 1
+            details.append({
+                "timestamp": _format_timestamp(s.start),
+                "speaker": s.speaker,
+                "text": s.text,
+                "sentiment": s.sentiment
+            })
+        out["sentiment"] = {"counts": counts, "details": details}
+    if topics:
+        out["topics"] = transcript.iab_categories.summary
+
+    return out
+
+if __name__ == "__main__":
+    mcp.run(transport="stdio")