Feature: whisper.cpp（音声認識）と pyannote-audio（話者識別）を組み合わせ
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# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Build Commands
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```bash
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# Standard build (Release by default on non-MSVC)
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cmake -B build
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cmake --build build -j --config Release
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# GPU backends
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cmake -B build -DGGML_CUDA=1 # NVIDIA CUDA
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cmake -B build -DGGML_VULKAN=1 # Vulkan (cross-vendor)
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cmake -B build -DGGML_METAL=1 # Apple Metal
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cmake -B build -DGGML_BLAS=1 # CPU via OpenBLAS
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# Optional features
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cmake -B build -DWHISPER_SDL2=ON # Enable SDL2 for real-time audio (stream example)
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cmake -B build -DWHISPER_CURL=ON # Enable libcurl for model download
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cmake -B build -DWHISPER_COREML=ON # Apple Core ML encoder (Apple Silicon only)
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# Sanitizers
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cmake -B build -DWHISPER_SANITIZE_ADDRESS=ON
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cmake -B build -DWHISPER_SANITIZE_THREAD=ON
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cmake -B build -DWHISPER_SANITIZE_UNDEFINED=ON
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```
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## Running Tests
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Tests require model files (downloaded separately) and use CTest:
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```bash
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# Build with tests enabled (on by default when building standalone)
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cmake -B build -DWHISPER_BUILD_TESTS=ON
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cmake --build build -j --config Release
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# Run all tests
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cd build && ctest
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# Run a specific test by label
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cd build && ctest -L tiny
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# Run single integration test manually (requires model at models/for-tests-ggml-tiny.en.bin)
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./build/bin/whisper-cli -m models/for-tests-ggml-tiny.en.bin -f samples/jfk.wav
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# Run the unit VAD test binary
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./build/bin/test-vad
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```
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## Downloading Models
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```bash
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# Download a pre-converted ggml model
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bash ./models/download-ggml-model.sh base.en # or: tiny, small, medium, large-v3, etc.
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# Download VAD model (for --vad flag)
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bash ./models/download-vad-model.sh silero-v6.2.0
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# Convenience Makefile targets (downloads model + builds + runs on samples/)
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make base.en
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make tiny
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```
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## Transcribing Audio
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Audio must be 16-bit WAV at 16 kHz. Convert with ffmpeg:
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```bash
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ffmpeg -i input.mp3 -ar 16000 -ac 1 -c:a pcm_s16le output.wav
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./build/bin/whisper-cli -m models/ggml-base.en.bin -f output.wav
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```
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## Quantization
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```bash
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./build/bin/quantize models/ggml-base.en.bin models/ggml-base.en-q5_0.bin q5_0
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./build/bin/whisper-cli -m models/ggml-base.en-q5_0.bin -f samples/jfk.wav
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```
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---
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## Architecture Overview
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### Two-layer design
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```
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include/whisper.h ← Public C API (cross-language compatible)
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src/whisper.cpp ← Whisper model: audio preprocessing, encoder, decoder, beam search
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src/whisper-arch.h ← Tensor name map (encoder/decoder/cross-attention weight paths in ggml format)
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ggml/ ← Tensor math library (git subtree from ggml-org/ggml)
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```
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`whisper_context` holds the loaded model weights (shared, read-only across threads). `whisper_state` holds per-inference mutable state (KV cache, mel buffers). You can create multiple states from one context for parallel inference.
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### ggml subdirectory
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`ggml/` is a git subtree (synced via `sync` commits). Do not edit it directly unless you are making changes intended to be upstreamed. Hardware backends live in `ggml/src/`:
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- `ggml-cpu/` — generic CPU with NEON/AVX/VSX intrinsics
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- `ggml-cuda/` — CUDA kernels
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- `ggml-metal/` — Metal shaders (Apple)
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- `ggml-vulkan/` — Vulkan compute shaders
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- `ggml-sycl/` — SYCL (Intel)
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### Whisper pipeline (inside `src/whisper.cpp`)
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1. **Audio preprocessing** — raw PCM → log-Mel spectrogram (80 mel bins, 30-second chunks at 16 kHz)
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2. **Encoder** — convolutional feature extraction + transformer encoder; optional Core ML / OpenVINO offload
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3. **Decoder** — autoregressive transformer decoder with optional beam search, temperature fallback, and cross-attention timestamps
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4. **VAD** — optional pre-pass using Silero-VAD to skip silence before encoding
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### Examples (`examples/`)
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Shared utilities used by all examples live at the top level of `examples/`:
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- `common.h / common.cpp` — CLI arg parsing, vocab helpers
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- `common-whisper.h / common-whisper.cpp` — WAV reading, timestamp formatting
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- `common-sdl.h / common-sdl.cpp` — SDL2 audio capture (stream example only)
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- `grammar-parser.h / grammar-parser.cpp` — GBNF grammar parsing for constrained decoding
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Key example binaries:
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| Binary | Source | Purpose |
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|--------|--------|---------|
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| `whisper-cli` | `examples/cli/` | Primary file transcription tool |
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| `whisper-stream` | `examples/stream/` | Real-time mic input (needs SDL2) |
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| `whisper-server` | `examples/server/` | HTTP API server |
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| `whisper-bench` | `examples/bench/` | Inference benchmarking |
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| `quantize` | `examples/quantize/` | Model quantization |
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| `vad-speech-segments` | `examples/vad-speech-segments/` | VAD-only segment extraction |
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### Bindings (`bindings/`)
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Language bindings wrap the C API in `include/whisper.h`:
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- `bindings/go/` — Go
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- `bindings/java/` — JNI (used by the Android example)
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- `bindings/javascript/` — WASM/Node.js (built via Emscripten)
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- `bindings/ruby/` — Ruby
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### Model format
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Models are stored in custom `ggml` binary format (not GGUF). The original OpenAI PyTorch weights are converted with `models/convert-pt-to-ggml.py`. Pre-converted models are available from HuggingFace (`ggerganov/whisper.cpp`). Tensor names follow the pattern defined in `src/whisper-arch.h`.
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## Windows-specific notes
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The project builds with MSVC. The CMakeLists.txt defines `_DISABLE_CONSTEXPR_MUTEX_CONSTRUCTOR` on Windows to work around an MSVC STL issue that causes crashes in the Java bindings. Several MSVC warnings are suppressed project-wide (see the `MSVC_WARNING_FLAGS` block at the bottom of `CMakeLists.txt`).
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#!/usr/bin/env python3
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"""
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Meeting transcription with per-speaker labels.
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Pipeline:
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1. whisper.cpp (whisper-cli) -> timestamped transcript JSON
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2. pyannote-audio -> speaker segments
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3. merge by timestamp overlap -> labelled output
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Usage:
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python diarize.py -f meeting.wav -m large-v3 --hf-token hf_xxx
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Requirements:
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pip install -r requirements-diarize.txt
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(HuggingFace token with pyannote/speaker-diarization-3.1 terms accepted)
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"""
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import argparse
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import json
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import os
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import subprocess
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import sys
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import tempfile
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from pathlib import Path
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import soundfile as sf
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import torch
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from pyannote.audio import Pipeline
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# ---------------------------------------------------------------------------
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# Step 1: whisper.cpp transcription
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# ---------------------------------------------------------------------------
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def find_whisper_cli() -> str:
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script_dir = Path(__file__).parent
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repo_root = script_dir.parent.parent
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candidates = [
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repo_root / "build" / "bin" / "Release" / "whisper-cli.exe", # Windows MSVC
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repo_root / "build" / "bin" / "whisper-cli.exe", # Windows MinGW
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repo_root / "build" / "bin" / "whisper-cli", # Linux/Mac
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]
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for p in candidates:
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if p.exists():
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return str(p)
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raise FileNotFoundError(
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"whisper-cli not found. Build the project first:\n"
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" cmake -B build && cmake --build build -j --config Release"
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)
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def run_whisper(audio_path: str, model: str, language: str, threads: int) -> list:
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cli = find_whisper_cli()
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repo_root = Path(__file__).parent.parent.parent
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model_path = repo_root / "models" / f"ggml-{model}.bin"
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if not model_path.exists():
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raise FileNotFoundError(
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f"Model not found: {model_path}\n"
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f"Download with: bash models/download-ggml-model.sh {model}"
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)
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with tempfile.TemporaryDirectory() as tmpdir:
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out_base = os.path.join(tmpdir, "out")
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cmd = [
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cli,
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"-m", str(model_path),
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"-f", audio_path,
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"-l", language,
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"-t", str(threads),
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"--output-json",
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"--output-file", out_base,
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]
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result = subprocess.run(cmd, capture_output=True, text=True)
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if result.returncode != 0:
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raise RuntimeError(f"whisper-cli failed:\n{result.stderr}")
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json_path = out_base + ".json"
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if not os.path.exists(json_path):
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raise RuntimeError(
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"whisper-cli did not produce a JSON file. "
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"stderr:\n" + result.stderr
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)
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with open(json_path, encoding="utf-8") as f:
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data = json.load(f)
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return data.get("transcription", [])
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# ---------------------------------------------------------------------------
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# Step 2: pyannote-audio speaker diarization
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# ---------------------------------------------------------------------------
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def run_diarization(audio_path: str, hf_token: str, num_speakers: int | None) -> list:
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print("Loading pyannote speaker-diarization-3.1 (CPU) ...", file=sys.stderr)
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pipeline = Pipeline.from_pretrained(
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"pyannote/speaker-diarization-3.1",
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token=hf_token,
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)
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pipeline.to(torch.device("cpu"))
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# Use soundfile to avoid the torchcodec/FFmpeg dependency on Windows.
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# pyannote accepts a pre-loaded {'waveform': Tensor, 'sample_rate': int} dict.
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waveform, sample_rate = sf.read(audio_path, dtype="float32", always_2d=True)
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waveform_tensor = torch.from_numpy(waveform.T) # (channels, time)
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audio_input = {"waveform": waveform_tensor, "sample_rate": sample_rate}
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kwargs = {}
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if num_speakers is not None:
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kwargs["num_speakers"] = num_speakers
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print("Running diarization ...", file=sys.stderr)
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result = pipeline(audio_input, **kwargs)
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# pyannote.audio 4.x returns DiarizeOutput; the Annotation is in .speaker_diarization
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annotation = result.speaker_diarization
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segments = []
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for turn, _, speaker in annotation.itertracks(yield_label=True):
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segments.append({"start": turn.start, "end": turn.end, "speaker": speaker})
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return segments
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# ---------------------------------------------------------------------------
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# Step 3: merge by timestamp overlap
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# ---------------------------------------------------------------------------
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def assign_speakers(transcription: list, diarization: list) -> list:
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results = []
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for seg in transcription:
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# whisper offsets are in milliseconds
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t0 = seg["offsets"]["from"] / 1000.0
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t1 = seg["offsets"]["to"] / 1000.0
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text = seg.get("text", "").strip()
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if not text:
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continue
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best_speaker = "UNKNOWN"
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best_overlap = 0.0
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for d in diarization:
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overlap = min(t1, d["end"]) - max(t0, d["start"])
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if overlap > best_overlap:
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best_overlap = overlap
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best_speaker = d["speaker"]
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results.append({"start": t0, "end": t1, "speaker": best_speaker, "text": text})
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return results
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# ---------------------------------------------------------------------------
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# Output formatting
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# ---------------------------------------------------------------------------
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def _fmt_time(seconds: float) -> str:
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m, s = divmod(int(seconds), 60)
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h, m = divmod(m, 60)
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return f"{h:02d}:{m:02d}:{s:02d}"
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def format_output(segments: list) -> str:
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lines = []
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for seg in segments:
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ts = f"[{_fmt_time(seg['start'])} --> {_fmt_time(seg['end'])}]"
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lines.append(f"{ts} {seg['speaker']}: {seg['text']}")
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return "\n".join(lines)
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def format_json(segments: list) -> str:
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return json.dumps(segments, ensure_ascii=False, indent=2)
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# ---------------------------------------------------------------------------
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# CLI
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# ---------------------------------------------------------------------------
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def parse_args():
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p = argparse.ArgumentParser(description="Whisper.cpp + pyannote diarization pipeline")
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p.add_argument("-f", "--file", required=True, help="Input WAV file (16 kHz, 16-bit)")
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p.add_argument("-m", "--model", default="large-v3", help="ggml model name (default: large-v3)")
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p.add_argument("-l", "--language", default="ja", help="Language code (default: ja)")
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p.add_argument("-t", "--threads", type=int, default=4, help="whisper-cli thread count")
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p.add_argument("--hf-token", required=True, help="HuggingFace access token")
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p.add_argument("--num-speakers", type=int, default=None, help="Known speaker count (optional)")
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p.add_argument("--output-json", action="store_true", help="Output JSON instead of plain text")
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return p.parse_args()
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def main():
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args = parse_args()
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if not os.path.exists(args.file):
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sys.exit(f"Audio file not found: {args.file}")
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print("Step 1/3: Transcribing with whisper.cpp ...", file=sys.stderr)
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transcription = run_whisper(args.file, args.model, args.language, args.threads)
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print("Step 2/3: Diarizing speakers with pyannote ...", file=sys.stderr)
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diarization = run_diarization(args.file, args.hf_token, args.num_speakers)
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print("Step 3/3: Merging results ...", file=sys.stderr)
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segments = assign_speakers(transcription, diarization)
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if args.output_json:
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print(format_json(segments))
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else:
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print(format_output(segments))
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if __name__ == "__main__":
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main()
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# Install in two steps:
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# Step A (PyTorch CPU wheel):
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# pip install torch torchaudio --index-url https://download.pytorch.org/whl/cpu
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# Step B (remaining packages from PyPI):
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# pip install -r requirements-diarize.txt
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# Speaker diarization
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pyannote.audio>=3.1.0
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# Hugging Face model hub access
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huggingface_hub>=0.20.0
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# Audio reading (avoids torchcodec/FFmpeg dependency on Windows)
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soundfile>=0.12.0
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# pyannote-audio 導入手順
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whisper.cpp（音声認識）と pyannote-audio（話者識別）を組み合わせた、
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**日本語会議録音の話者別文字起こしパイプライン**の構築手順です。
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## 前提条件
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- OS: Windows 11
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- Anaconda / Miniconda がインストール済みであること
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- Git for Windows がインストール済みであること
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- リポジトリ: `c:\work\30.Projects\102.AI_Projects\whisper.cpp\whisper.cpp`
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---
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## ステップ 1: whisper.cpp のビルド
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```bash
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cd c:\work\30.Projects\102.AI_Projects\whisper.cpp\whisper.cpp
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cmake -B build
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cmake --build build -j --config Release
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```
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ビルド完了後、以下のバイナリが生成されます：
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```
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build/bin/Release/whisper-cli.exe
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```
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---
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## ステップ 2: Python 仮想環境の作成
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> **注意:** Python 3.13 は pyannote.audio 非対応のため、**3.11 を使用すること**。
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```bash
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conda create -n whisper-diarize python=3.11 -y
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```
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---
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## ステップ 3: パッケージのインストール
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`--index-url` の適用範囲の問題により、**2段階でインストール**する必要があります。
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### Step A: PyTorch（CPU専用ビルド）
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```bash
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conda run -n whisper-diarize pip install torch torchaudio --index-url https://download.pytorch.org/whl/cpu
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```
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### Step B: pyannote.audio および関連パッケージ
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```bash
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conda run -n whisper-diarize pip install -r examples/python/requirements-diarize.txt
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```
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`requirements-diarize.txt` の内容（`examples/python/requirements-diarize.txt`）：
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```
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# Speaker diarization
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pyannote.audio>=3.1.0
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# Hugging Face model hub access
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huggingface_hub>=0.20.0
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# Audio reading（Windows で torchcodec/FFmpeg 不要にするための回避策）
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soundfile>=0.12.0
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```
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> **Windows の注意事項:**
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> pyannote.audio 4.x は音声読み込みに `torchcodec`（FFmpeg 必須）を使用しますが、
|
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> conda-forge の FFmpeg は Windows 日本語環境でインストールエラーになる場合があります。
|
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> 代わりに `soundfile` でWAVを読み込み、テンソルとして直接 pyannote に渡す方式を採用しています。
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|
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### インストール確認
|
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|
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```bash
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C:/work/60.Tools/Anaconda/miniconda3/envs/whisper-diarize/python.exe -c ^
|
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"import torch, pyannote.audio, soundfile; ^
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print('torch:', torch.__version__); ^
|
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print('pyannote:', pyannote.audio.__version__); ^
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print('soundfile:', soundfile.__version__); ^
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print('CPU only:', not torch.cuda.is_available())"
|
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```
|
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|
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期待される出力：
|
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|
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```
|
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torch: 2.11.0+cpu
|
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pyannote.audio: 4.0.4
|
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soundfile: 0.13.1
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CPU only: True
|
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```
|
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|
||||
---
|
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|
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## ステップ 4: HuggingFace トークンの取得と利用規約への同意
|
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|
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pyannote のモデルはゲート付きリポジトリのため、以下の手順が**全て必要**です。
|
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|
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### 4-1. HuggingFace アカウント作成
|
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|
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https://huggingface.co でサインアップ（既存アカウントがあればスキップ）。
|
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|
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### 4-2. 利用規約への同意（3つ全て必要）
|
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|
||||
以下の各ページにアクセスし、**"Agree and access repository"** をクリックする。
|
||||
|
||||
| モデル | URL |
|
||||
|--------|-----|
|
||||
| speaker-diarization-3.1 | https://huggingface.co/pyannote/speaker-diarization-3.1 |
|
||||
| segmentation-3.0 | https://huggingface.co/pyannote/segmentation-3.0 |
|
||||
| speaker-diarization-community-1 | https://huggingface.co/pyannote/speaker-diarization-community-1 |
|
||||
|
||||
> **注意:** 3つ目の `speaker-diarization-community-1` は pyannote.audio 4.x から追加された依存リポジトリです。
|
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> 同意しないと実行時に `403 Forbidden` エラーが発生します。
|
||||
|
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### 4-3. アクセストークンの発行
|
||||
|
||||
1. https://huggingface.co/settings/tokens にアクセス
|
||||
2. "New token" をクリック
|
||||
3. 権限: `read` を選択して作成
|
||||
4. `hf_` で始まるトークン文字列を控えておく
|
||||
|
||||
---
|
||||
|
||||
## ステップ 5: 日本語モデルのダウンロード
|
||||
|
||||
```bash
|
||||
cd c:\work\30.Projects\102.AI_Projects\whisper.cpp\whisper.cpp
|
||||
|
||||
# 精度重視（推奨、2.9GB）
|
||||
bash models/download-ggml-model.sh large-v3
|
||||
|
||||
# 速度重視（動作確認用、142MB）
|
||||
bash models/download-ggml-model.sh base
|
||||
```
|
||||
|
||||
| モデル | サイズ | 日本語精度 |
|
||||
|--------|--------|------------|
|
||||
| large-v3 | 2.9 GB | 最高 |
|
||||
| medium | 1.5 GB | 高 |
|
||||
| small | 466 MB | 中 |
|
||||
| base | 142 MB | 低（動作確認用途） |
|
||||
|
||||
---
|
||||
|
||||
## ステップ 6: 音声ファイルの準備
|
||||
|
||||
whisper.cpp は **16kHz / 16bit / モノラル WAV** のみ対応。
|
||||
他の形式の場合は ffmpeg で変換する。
|
||||
|
||||
```bash
|
||||
ffmpeg -i 会議録音.mp4 -ar 16000 -ac 1 -c:a pcm_s16le 会議録音.wav
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ステップ 7: 実行
|
||||
|
||||
スクリプト: `examples/python/diarize.py`
|
||||
|
||||
```bash
|
||||
cd c:\work\30.Projects\102.AI_Projects\whisper.cpp\whisper.cpp
|
||||
|
||||
C:/work/60.Tools/Anaconda/miniconda3/envs/whisper-diarize/python.exe \
|
||||
examples/python/diarize.py \
|
||||
-f 会議録音.wav \
|
||||
-m large-v3 \
|
||||
--hf-token hf_xxxxxxxxxxxx \
|
||||
--language ja
|
||||
```
|
||||
|
||||
### オプション一覧
|
||||
|
||||
| オプション | 省略形 | デフォルト | 説明 |
|
||||
|---|---|---|---|
|
||||
| `--file` | `-f` | （必須） | 入力WAVファイル |
|
||||
| `--model` | `-m` | `large-v3` | ggmlモデル名 |
|
||||
| `--language` | `-l` | `ja` | 言語コード |
|
||||
| `--hf-token` | | （必須） | HuggingFace トークン |
|
||||
| `--num-speakers` | | 自動検出 | 参加者数（既知の場合に指定すると精度向上） |
|
||||
| `--threads` | `-t` | `4` | whisper-cli スレッド数 |
|
||||
| `--output-json` | | OFF | JSON形式で出力 |
|
||||
|
||||
### 出力例
|
||||
|
||||
```
|
||||
[00:00:00 --> 00:00:03] SPEAKER_00: 本日はお集まりいただきありがとうございます。
|
||||
[00:00:03 --> 00:00:07] SPEAKER_01: よろしくお願いします。
|
||||
[00:00:07 --> 00:00:12] SPEAKER_00: では、議題に入りましょう。
|
||||
[00:00:12 --> 00:00:18] SPEAKER_02: 先週の進捗を報告します。
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## トラブルシューティング
|
||||
|
||||
### `torchcodec` の警告が大量に出る
|
||||
|
||||
```
|
||||
UserWarning: torchcodec is not installed correctly so built-in audio decoding will fail.
|
||||
```
|
||||
|
||||
**→ 無視して問題ありません。**
|
||||
`soundfile` による回避策が有効になっているため、実際の動作に影響しません。
|
||||
|
||||
### `403 Forbidden` / `GatedRepoError`
|
||||
|
||||
pyannote モデルへのアクセス権がありません。
|
||||
**→ ステップ 4-2 の3リポジトリ全てに同意済みか確認してください。**
|
||||
|
||||
### `TypeError: Pipeline.from_pretrained() got an unexpected keyword argument 'use_auth_token'`
|
||||
|
||||
pyannote.audio 4.x で `use_auth_token` 引数が廃止されました。
|
||||
**→ `token=` に変更してください（`diarize.py` は修正済み）。**
|
||||
|
||||
### `AttributeError: 'DiarizeOutput' object has no attribute 'itertracks'`
|
||||
|
||||
pyannote.audio 4.x で出力型が `DiarizeOutput` に変更されました。
|
||||
**→ `result.speaker_diarization.itertracks()` を使用してください（`diarize.py` は修正済み）。**
|
||||
|
||||
---
|
||||
|
||||
## 環境情報（動作確認済み）
|
||||
|
||||
| 項目 | バージョン |
|
||||
|---|---|
|
||||
| OS | Windows 11 Pro 10.0.26200 |
|
||||
| Python | 3.11 (conda) |
|
||||
| torch | 2.11.0+cpu |
|
||||
| pyannote.audio | 4.0.4 |
|
||||
| soundfile | 0.13.1 |
|
||||
| whisper.cpp | v1.8.4 (master) |
|
||||
| whisper-cliモデル | ggml-base.bin / ggml-large-v3.bin |
|
||||
Loading…
Reference in New Issue