Ryan Malloy
ec4e1b6093
Stream-restore mute: pw-play streams were being silently restored to 0% volume by PulseAudio's stream-restore module matching the music role key, producing audible-but-clean-exit playback. audio.py now bumps each new pw-play sink-input to 100% as a background task; the new value re-stamps stream-restore on stream end. Entry tone overlap: when speak() arrived during prior playback, its entry tone played immediately from the handler and overlapped the ongoing audio. queue.is_idle() now gates this — tone plays now when idle (preserves latency-hiding intent), else defers to the consumer via _WorkItem.entry_tone, which plays it right before the item's audio. Chunked path defers only on chunk 0. Docker: llama-server gated behind a `with-orpheus` compose profile; mcspeak.depends_on uses required:false. `make up` defaults to kokoro-only with no GPU dependency; `make up-with-orpheus` runs the full stack. Rename dootie-internal network → mcspeak-internal (auto-created per-stack via internal:true). Add 127.0.0.1:8371 port mapping for local MCP clients. README: fix claude mcp add command (was stdio, must be --transport http); document kokoro-only default and the with-orpheus path; add Kokoro model download step. .env.example added so `cp .env.example .env` matches the documented setup.
43 lines
1.2 KiB
Makefile
43 lines
1.2 KiB
Makefile
# .env is optional in kokoro-only mode (only needed for ORPHEUS_GGUF_PATH).
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# The leading dash makes Make silently tolerate a missing file.
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-include .env
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export
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.PHONY: build up up-with-orpheus down logs restart status bench
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build:
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docker compose build
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up: build
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docker compose up -d
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@sleep 2
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docker compose logs --tail 20
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up-with-orpheus: build
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docker compose --profile with-orpheus up -d
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@sleep 2
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docker compose logs --tail 20
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down:
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docker compose down
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logs:
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docker compose logs -f
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restart:
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docker compose restart
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@sleep 2
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docker compose logs --tail 20
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status:
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@docker compose ps
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@echo "---"
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@curl -s http://localhost:8371/mcp 2>/dev/null | head -5 || echo "Server not responding"
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bench:
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@echo "Benchmarking llama-server throughput..."
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@docker exec orpheus-llama-server curl -s "http://127.0.0.1:8081/v1/completions" \
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-H "Content-Type: application/json" \
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-d '{"prompt":"<|audio|>tara: Hello, how are you doing today?<|eot_id|>","max_tokens":500,"stream":false}' | \
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python3 -c "import sys,json; d=json.load(sys.stdin); u=d['usage']; t=d.get('timings',{}); print(f\"{u['completion_tokens']} tokens, {t.get('predicted_per_second',0):.1f} tok/s\")"
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