Add speak-summary skill: text to listenable MP3 via local CPU TTS (#2797)

Adds a skill that converts text, markdown, or the output of another
skill into an MP3 using Kyutai pocket-tts, a small neural TTS model
that runs on CPU.

No existing skill in the collection generates audio, so this fills a
gap rather than duplicating one. It is designed as a terminal step in
a chain: roundup, daily-prep, or meeting-minutes produce the text,
speak-summary makes it listenable.

Two details worth calling out:

- The bulk of SKILL.md is guidance on rewriting written prose for the
  ear before synthesising. Feeding markdown straight into a TTS engine
  produces something technically correct and unlistenable, so that
  step carries most of the value.
- Synthesis is local and CPU-only, so nothing is sent to a cloud
  speech service and the skill works unattended in a headless
  container as well as on a laptop.

The bundled script bootstraps pocket-tts into a cached virtualenv on
first use, selecting a Python in the supported >=3.10,<3.15 range
rather than assuming python3 qualifies, and failing with actionable
guidance when none is available.

Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 1a70aa08-b622-4825-ad63-5a12370add1f
This commit is contained in:
Sam Rowe
2026-08-27 12:04:35 +10:00
committed by GitHub
co-authored by Copilot App
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---
name: speak-summary
description: 'Convert text, markdown, or a summary produced by another skill into a listenable MP3 using local CPU-only neural text-to-speech. Rewrites written prose for the ear before synthesising. Use when the user asks to "read this out", "turn this into audio", "make an MP3", "I want to listen to this", "podcast version", or wants a spoken digest for a commute or breakfast.'
---
# Speak Summary
Turn written text into audio someone will actually want to listen to.
This skill is deliberately a **terminal step in a chain**. Another skill (or you)
produces the text; this one makes it listenable. It pairs naturally with
`roundup`, `daily-prep`, `meeting-minutes`, or any summarisation work.
Everything runs locally on CPU. No text is sent to a cloud speech service, which
matters when the content is confidential, and it means the skill works in a
headless cloud agent or CI container just as well as on a laptop.
## Prerequisites
The synthesis engine is [Kyutai `pocket-tts`](https://github.com/kyutai-labs/pocket-tts),
a small neural TTS model designed to run on CPUs.
The bundled script installs it automatically into a cached virtualenv on first
use, so usually you need do nothing. To install it explicitly:
```bash
pip install pocket-tts # any platform
brew install pocket-tts # macOS, if preferred
```
`pocket-tts` requires **Python >=3.10 and <3.15**. The script searches for a
compatible interpreter rather than assuming `python3` is one — worth knowing if
you are on a very new Python, where installation would otherwise fail.
You also need an encoder. `ffmpeg` is strongly preferred (`brew install ffmpeg`
or `apt-get install -y ffmpeg`); on macOS the script falls back to the built-in
`afconvert` and emits `.m4a` instead of `.mp3`.
The first run downloads the model (~1GB) from Hugging Face. After that it is
fully offline and synthesises roughly 6x faster than real-time.
## The important step: rewrite for the ear
**Do not feed written text straight into the synthesiser.** Prose that reads well
on screen is tiring to listen to. Rewriting it first is what separates a useful
audio digest from an unlistenable one.
Produce a spoken script that:
- **Opens with orientation.** What this is, what it covers, roughly how long it runs.
- **Replaces bullets with connective prose.** "First… The bigger one is… Finally…" — a listener has no visual structure to lean on, so carry it in the language.
- **Expands abbreviations on first use.** "PR" becomes "pull request", "CI" becomes "continuous integration". Acronyms that read fine are noise when spoken.
- **Speaks dates and numbers naturally.** "the twentieth of August", not "2026-08-20". "About three thousand", not "2,847".
- **Never reads URLs aloud.** Say "linked in the written version" instead.
- **Uses short sentences.** Split anything past roughly 25 words.
- **Signposts transitions.** "Turning to the product side…", "Two things need your attention…".
- **Ends with the actions.** Recap what the listener should do, since that is what they need to retain and they cannot scroll back.
- **Drops anything purely visual.** Tables, code blocks, and diagrams should be summarised in a sentence or omitted, never read out.
Write this spoken script to its own `.txt` file. Keep the original written
version with its links intact — the audio is a companion to it, not a
replacement. The user will want to click through later.
## Synthesise
```bash
./scripts/tts.sh <input.txt> <output.mp3> [voice.safetensors]
```
The script strips any residual markdown, splits the text on sentence boundaries
into ~600 character chunks (quality degrades on long single inputs), synthesises
each chunk, and concatenates the result into a mono MP3 at 96kbps — small enough
to sync to a phone, good enough for speech.
Environment overrides:
| Variable | Purpose |
|---|---|
| `SPEAK_TTS_BIN` | Path to a specific `pocket-tts` binary; skips all auto-detection. |
| `SPEAK_TTS_HOME` | Where to create/find the cached virtualenv. Default `~/.cache/speak-summary/venv`. |
## Voices
The default English voice is `alba`. To use a different one, `pocket-tts`
supports voice cloning from a short clean audio sample:
```bash
pocket-tts export-voice --help
```
Pass the resulting `.safetensors` file as the third argument to the script.
Only clone a voice you have the rights to use. Do not clone a real person's
voice — colleague, customer, or public figure — without their explicit consent.
## Output
- Default to `~/Music/Briefings/` unless the user says otherwise; it is easy to point a phone or podcast app at.
- Name files `<subject>-<YYYY-MM-DD>.mp3`.
- Report the path, duration, and size.
- Offer to play it: `afplay <path>` on macOS, `ffplay -nodisp -autoexit <path>` elsewhere.
## Length guidance
Aim for 46 minutes for a routine digest, which is roughly 600900 spoken words
at a natural pace. If the source would run past about 10 minutes, say so and
offer either a tighter edit or a split into multiple files — attention drops off
sharply beyond that for informational audio.
## Chaining onto other skills
The natural pattern is *gather → summarise → speak*:
- `roundup``speak-summary` — a spoken version of the status briefing.
- `daily-prep``speak-summary` — tomorrow's schedule, listened to tonight.
- `meeting-minutes``speak-summary` — catch up on a meeting you missed.
When invoked as part of a chain, do not re-summarise. The upstream skill owns
what to say; this skill owns how it sounds. Take its output, rewrite it for the
ear, and synthesise.
To run unattended (a briefing waiting before breakfast), schedule the upstream
skill with a workflow and have it finish by calling this one.
## Troubleshooting
**Audio cuts off mid-sentence.** A chunk exceeded the model's comfortable length.
Shorten the sentences in the spoken script.
**Words mispronounced.** Spell them phonetically in the input — "Kubernetes" as
"koo-ber-net-eez". This is a normal part of preparing a spoken script.
**First run is slow.** That is the one-off model download. Later runs start in
about a second.
**`pocket-tts` not found after install.** The virtualenv may be stale, or your
`python3` may be outside the supported 3.103.14 range. Delete
`~/.cache/speak-summary/venv` and re-run, or point `SPEAK_TTS_BIN` at a known binary.
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#!/usr/bin/env bash
# Convert a plain-text briefing into an MP3 using local neural TTS (Kyutai pocket-tts).
#
# Usage: tts.sh <input.txt> <output.mp3> [voice.safetensors]
#
# CPU-only and fully offline after the first model download, so it runs the same
# on an Apple Silicon Mac and in a Linux cloud agent container. No text is sent
# to any cloud TTS service.
#
# Resolution order for the engine:
# 1. pocket-tts already on PATH (e.g. `brew install pocket-tts`)
# 2. a cached venv at $SPEAK_TTS_HOME (default ~/.cache/speak-summary/venv)
# 3. create that venv and `pip install pocket-tts`
# Set SPEAK_TTS_BIN to point at a specific pocket-tts binary to skip all this.
set -euo pipefail
IN="${1:?usage: tts.sh <input.txt> <output.mp3> [voice.safetensors]}"
OUT="${2:?usage: tts.sh <input.txt> <output.mp3> [voice.safetensors]}"
VOICE="${3:-}"
TTS_HOME="${SPEAK_TTS_HOME:-$HOME/.cache/speak-summary/venv}"
# pocket-tts supports Python >=3.10,<3.15. The system python3 is often outside
# that range, so search for a usable interpreter rather than assuming.
find_python() {
for c in python3.14 python3.13 python3.12 python3.11 python3.10 python3; do
p="$(command -v "$c" 2>/dev/null)" || continue
"$p" -c 'import sys; raise SystemExit(0 if (3,10) <= sys.version_info < (3,15) else 1)' 2>/dev/null \
&& { echo "$p"; return 0; }
done
return 1
}
resolve_tts() {
if [ -n "${SPEAK_TTS_BIN:-}" ]; then echo "$SPEAK_TTS_BIN"; return; fi
if command -v pocket-tts >/dev/null 2>&1; then command -v pocket-tts; return; fi
if [ -x "$TTS_HOME/bin/pocket-tts" ]; then echo "$TTS_HOME/bin/pocket-tts"; return; fi
PY="$(find_python)" || {
cat >&2 <<'MSG'
No suitable Python found. pocket-tts requires Python >=3.10 and <3.15.
Install one (e.g. 'brew install python@3.14' or 'apt-get install -y python3.12-venv'),
or install pocket-tts yourself and point SPEAK_TTS_BIN at the binary.
MSG
exit 1
}
echo "pocket-tts not found; creating a virtualenv at $TTS_HOME using $PY (one-off, a few minutes)..." >&2
"$PY" -m venv "$TTS_HOME" >&2 || { echo "Failed to create virtualenv (is the venv module installed?)." >&2; exit 1; }
"$TTS_HOME/bin/pip" install --quiet --upgrade pip >&2
"$TTS_HOME/bin/pip" install --quiet pocket-tts >&2 || { echo "Failed to install pocket-tts." >&2; exit 1; }
[ -x "$TTS_HOME/bin/pocket-tts" ] || { echo "pocket-tts install completed but the binary is missing." >&2; exit 1; }
echo "$TTS_HOME/bin/pocket-tts"
}
TTS="$(resolve_tts)"
[ -x "$TTS" ] || { echo "TTS engine not executable: $TTS" >&2; exit 1; }
# Encoder: prefer ffmpeg; fall back to macOS afconvert (AAC in .m4a) if absent.
ENCODER=""
if command -v ffmpeg >/dev/null 2>&1; then ENCODER="ffmpeg"
elif command -v afconvert >/dev/null 2>&1; then ENCODER="afconvert"
else
echo "Need ffmpeg to encode audio. Install with 'brew install ffmpeg' or 'apt-get install -y ffmpeg'." >&2
exit 1
fi
WORK="$(mktemp -d)"
trap 'rm -rf "$WORK"' EXIT
# pocket-tts quality degrades on very long inputs, so split into ~600-char chunks
# on sentence boundaries, synthesise each, then concatenate.
python3 - "$IN" "$WORK" <<'PY'
import re, sys, pathlib
src = pathlib.Path(sys.argv[1]).read_text()
work = pathlib.Path(sys.argv[2])
# Strip markdown that would otherwise be read aloud as noise.
src = re.sub(r'```.*?```', ' ', src, flags=re.S) # fenced code
src = re.sub(r'\[([^\]]+)\]\([^)]+\)', r'\1', src) # links -> label
src = re.sub(r'^\s*#{1,6}\s*', '', src, flags=re.M) # headings
src = re.sub(r'^\s*[-*+]\s+', '', src, flags=re.M) # bullets
src = re.sub(r'[*_`>|~]+', '', src) # emphasis/code/table pipes
src = re.sub(r'https?://\S+', '', src) # bare URLs
src = re.sub(r'[ \t]+', ' ', src)
src = re.sub(r'\n{2,}', '\n\n', src).strip()
MAX = 600
chunks, cur = [], ""
for sent in re.split(r'(?<=[.!?])\s+|\n\n', src):
sent = sent.strip()
if not sent:
continue
# A single sentence longer than MAX is split on commas as a last resort.
while len(sent) > MAX:
cut = sent.rfind(',', 0, MAX)
cut = cut if cut > MAX // 2 else sent.rfind(' ', 0, MAX)
cut = cut if cut > 0 else MAX
if cur:
chunks.append(cur); cur = ""
chunks.append(sent[:cut].strip())
sent = sent[cut:].strip(' ,')
if len(cur) + len(sent) + 1 > MAX:
if cur:
chunks.append(cur)
cur = sent
else:
cur = f"{cur} {sent}".strip()
if cur:
chunks.append(cur)
if not chunks:
raise SystemExit("No speakable text found in input.")
for i, c in enumerate(chunks):
(work / f"chunk_{i:04d}.txt").write_text(c)
PY
N=0
for f in "$WORK"/chunk_*.txt; do
IDX="$(basename "$f" .txt)"
ARGS=(generate --quiet --text "$(cat "$f")" --output-path "$WORK/$IDX.wav")
[ -n "$VOICE" ] && ARGS+=(--voice "$VOICE")
N=$((N+1))
echo " synthesising chunk $N ..." >&2
"$TTS" "${ARGS[@]}" >/dev/null
echo "file '$WORK/$IDX.wav'" >> "$WORK/list.txt"
done
mkdir -p "$(dirname "$OUT")"
if [ "$ENCODER" = "ffmpeg" ]; then
ffmpeg -hide_banner -loglevel error -y -f concat -safe 0 -i "$WORK/list.txt" \
-c:a libmp3lame -b:a 96k -ar 24000 -ac 1 "$OUT"
else
# afconvert cannot concat, so join the WAVs first, then encode to AAC.
python3 - "$WORK" "$WORK/joined.wav" <<'PY'
import sys, wave, pathlib
work, out = pathlib.Path(sys.argv[1]), sys.argv[2]
parts = sorted(work.glob("chunk_*.wav"))
with wave.open(parts[0], 'rb') as w0:
params = w0.getparams()
with wave.open(out, 'wb') as o:
o.setparams(params)
for p in parts:
with wave.open(str(p), 'rb') as w:
o.writeframes(w.readframes(w.getnframes()))
PY
OUT="${OUT%.mp3}.m4a"
afconvert -f m4af -d aac -b 96000 "$WORK/joined.wav" "$OUT"
echo "Note: ffmpeg not available; wrote AAC (.m4a) instead of MP3." >&2
fi
SIZE="$(du -h "$OUT" | cut -f1)"
echo "Wrote $OUT ($SIZE, $N chunks)"