Add Deepgram MCP Server - speech-to-text and TTS
Python FastMCP server wrapping Deepgram API for audio transcription and text-to-speech. Supports 125+ multilingual voices, large file chunking via FFmpeg, formatted markdown output with speaker diarization, and Docker deployment on port 8009. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Alejandro Gutiérrez
230
deepgram-mcp/src/deepgram_mcp/splitter.py
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230
deepgram-mcp/src/deepgram_mcp/splitter.py
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"""FFmpeg-based audio splitting for files exceeding the Deepgram size limit."""
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import asyncio
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import json
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import shutil
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import tempfile
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from pathlib import Path
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async def get_audio_duration(file_path: Path) -> float:
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"""Get audio duration in seconds using ffprobe."""
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proc = await asyncio.create_subprocess_exec(
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"ffprobe",
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"-v", "quiet",
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"-print_format", "json",
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"-show_format",
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str(file_path),
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE,
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)
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stdout, stderr = await proc.communicate()
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if proc.returncode != 0:
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raise RuntimeError(
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f"ffprobe failed (exit {proc.returncode}): {stderr.decode().strip()}"
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)
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info = json.loads(stdout)
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return float(info["format"]["duration"])
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def get_file_size_mb(file_path: Path) -> float:
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"""Return the file size in megabytes."""
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return file_path.stat().st_size / (1024 * 1024)
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async def split_audio(
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file_path: Path,
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max_chunk_mb: int = 1500,
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) -> list[Path]:
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"""Split an audio file into chunks of approximately max_chunk_mb each.
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Uses ffmpeg's segment muxer with stream copy (no re-encoding).
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If the file is already under the limit, returns [file_path] unchanged.
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"""
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size_mb = get_file_size_mb(file_path)
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if size_mb <= max_chunk_mb:
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return [file_path]
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duration = await get_audio_duration(file_path)
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if duration <= 0:
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raise ValueError(f"Invalid audio duration: {duration}s")
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# Calculate segment time so each chunk is ~max_chunk_mb
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segment_time = int(duration * max_chunk_mb / size_mb)
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if segment_time < 1:
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segment_time = 1
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tmp_dir = Path(tempfile.mkdtemp(prefix="deepgram_chunks_"))
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ext = file_path.suffix or ".wav"
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pattern = str(tmp_dir / f"chunk_%03d{ext}")
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proc = await asyncio.create_subprocess_exec(
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"ffmpeg",
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"-i", str(file_path),
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"-f", "segment",
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"-segment_time", str(segment_time),
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"-c", "copy",
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"-v", "warning",
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pattern,
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE,
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)
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_, stderr = await proc.communicate()
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if proc.returncode != 0:
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shutil.rmtree(tmp_dir, ignore_errors=True)
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raise RuntimeError(
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f"ffmpeg split failed (exit {proc.returncode}): {stderr.decode().strip()}"
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)
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chunks = sorted(tmp_dir.glob(f"chunk_*{ext}"))
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if not chunks:
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shutil.rmtree(tmp_dir, ignore_errors=True)
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raise RuntimeError("ffmpeg produced no output chunks")
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return chunks
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def merge_transcription_results(
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results: list[dict],
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chunk_durations: list[float],
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) -> dict:
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"""Merge multiple Deepgram transcription responses into a single result.
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Adjusts all timestamps by cumulative offset so chunks stitch together
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correctly in the final timeline.
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"""
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if not results:
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return {}
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if len(results) == 1:
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return results[0]
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# Compute cumulative time offsets for each chunk
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offsets = [0.0]
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for dur in chunk_durations[:-1]:
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offsets.append(offsets[-1] + dur)
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merged_transcript_parts: list[str] = []
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merged_words: list[dict] = []
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merged_paragraphs: list[dict] = []
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merged_utterances: list[dict] = []
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merged_topics: list[dict] = []
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merged_entities: list[dict] = []
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merged_summaries: list[dict] = []
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merged_sentiments: list[dict] = []
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# Keep metadata from the first result as the base
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base = results[0].copy()
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for idx, result in enumerate(results):
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offset = offsets[idx]
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# Extract channel transcript data
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channels = (
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result.get("results", {}).get("channels", [])
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)
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if channels:
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alt = channels[0].get("alternatives", [{}])[0]
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transcript = alt.get("transcript", "")
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if transcript:
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merged_transcript_parts.append(transcript)
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for word in alt.get("words", []):
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adjusted = word.copy()
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adjusted["start"] = round(word.get("start", 0) + offset, 3)
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adjusted["end"] = round(word.get("end", 0) + offset, 3)
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merged_words.append(adjusted)
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for para in alt.get("paragraphs", {}).get("paragraphs", []):
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adjusted = para.copy()
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adjusted["start"] = round(para.get("start", 0) + offset, 3)
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adjusted["end"] = round(para.get("end", 0) + offset, 3)
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if "sentences" in adjusted:
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adjusted["sentences"] = [
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{
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**s,
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"start": round(s.get("start", 0) + offset, 3),
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"end": round(s.get("end", 0) + offset, 3),
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}
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for s in adjusted["sentences"]
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]
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merged_paragraphs.append(adjusted)
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# Utterances (diarization)
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for utt in result.get("results", {}).get("utterances", []):
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adjusted = utt.copy()
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adjusted["start"] = round(utt.get("start", 0) + offset, 3)
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adjusted["end"] = round(utt.get("end", 0) + offset, 3)
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if "words" in adjusted:
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adjusted["words"] = [
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{
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**w,
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"start": round(w.get("start", 0) + offset, 3),
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"end": round(w.get("end", 0) + offset, 3),
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}
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for w in adjusted["words"]
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]
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merged_utterances.append(adjusted)
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# Topics, entities, summaries, sentiments -- concatenate lists
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res = result.get("results", {})
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merged_topics.extend(res.get("topics", {}).get("segments", []))
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merged_entities.extend(res.get("entities", {}).get("segments", []))
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merged_summaries.extend(
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res.get("summary", {}).get("results", [])
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or res.get("summaries", [])
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)
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merged_sentiments.extend(
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res.get("sentiments", {}).get("segments", [])
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)
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# Assemble merged output
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if "results" not in base:
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base["results"] = {}
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merged_results = base["results"]
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# Rebuild channels
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if merged_results.get("channels"):
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channel = merged_results["channels"][0]
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alt = channel.get("alternatives", [{}])[0]
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alt["transcript"] = " ".join(merged_transcript_parts)
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alt["words"] = merged_words
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if merged_paragraphs:
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alt["paragraphs"] = {"paragraphs": merged_paragraphs}
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channel["alternatives"] = [alt]
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merged_results["channels"] = [channel]
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if merged_utterances:
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merged_results["utterances"] = merged_utterances
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if merged_topics:
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merged_results.setdefault("topics", {})["segments"] = merged_topics
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if merged_entities:
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merged_results.setdefault("entities", {})["segments"] = merged_entities
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if merged_summaries:
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merged_results["summaries"] = merged_summaries
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if merged_sentiments:
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merged_results.setdefault("sentiments", {})["segments"] = merged_sentiments
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return base
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def cleanup_chunks(chunk_paths: list[Path]) -> None:
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"""Delete temporary chunk files and their parent directory if it's a temp dir."""
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if not chunk_paths:
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return
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parent = chunk_paths[0].parent
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for path in chunk_paths:
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try:
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if path.is_file():
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path.unlink()
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except OSError:
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pass
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# Remove the temp directory if it's empty and looks like our temp dir
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if parent.name.startswith("deepgram_chunks_"):
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shutil.rmtree(parent, ignore_errors=True)
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