feat: compressor, more stastus verbosity, multiple ways to stop
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GPG key ID:
B97F30FDC4636357
8 changed files with 195 additions and 36 deletions
1
README.md
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1
README.md
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@ -0,0 +1 @@
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https://github.com/mrsobakin/speechd/
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@ -8,6 +8,7 @@ license = { text = "MIT" }
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dependencies = [
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"groq>=0.4.0",
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"numpy>=1.24.0",
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"packaging>=26.0",
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"sounddevice>=0.4.6",
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"soundfile>=0.12.0",
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"torch>=2.0.0",
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@ -1,15 +1,21 @@
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#!/bin/bash
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PIDFILE="${XDG_RUNTIME_DIR:-/tmp}/speechd.pid"
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if [ -f "$PIDFILE" ]; then
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PID=$(cat "$PIDFILE")
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if kill -0 "$PID" 2>/dev/null; then
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kill -USR1 "$PID"
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else
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echo "Daemon not running (stale pidfile)" >&2
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exit 1
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fi
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else
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[ -f "$PIDFILE" ] || {
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echo "Daemon not running (no pidfile)" >&2
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exit 1
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fi
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}
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PID=$(cat "$PIDFILE")
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kill -0 "$PID" 2>/dev/null || {
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echo "Daemon not running (stale pidfile)" >&2
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exit 1
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}
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case "$1" in
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"-n") kill -USR2 "$PID";;
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"-a") kill -ALRM "$PID";;
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*) kill -USR1 "$PID";;
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esac
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@ -21,6 +21,7 @@ class VoiceActivityDetector:
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def process(self, audio_data: np.ndarray) -> np.ndarray:
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if len(audio_data) == 0:
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print("No audio data!")
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return audio_data
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audio_float = audio_data.astype(np.float32) / 32768.0
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100
src/speechd/audio_processor.py
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src/speechd/audio_processor.py
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import logging
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import subprocess
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import numpy as np
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logger = logging.getLogger(__name__)
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class AudioProcessor:
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def __init__(self, peak_target: float = 1.0, silence_threshold: float = 1e-8):
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if not 0 < peak_target <= 1.0:
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raise ValueError("peak_target must be in range (0.0, 1.0]")
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self.peak_target = peak_target
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self.silence_threshold = silence_threshold
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@staticmethod
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def pcm16_to_float(audio_data: np.ndarray) -> np.ndarray:
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return audio_data.astype(np.float32) / 32768.0
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def normalize_peak(self, audio_data: np.ndarray) -> np.ndarray:
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if audio_data.size == 0:
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return audio_data
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peak = np.max(np.abs(audio_data))
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if peak <= self.silence_threshold:
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return audio_data
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normalized = audio_data * (self.peak_target / peak)
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np.clip(normalized, -1.0, 1.0, out=normalized)
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return normalized
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def normalize_pcm16_peak(self, audio_data: np.ndarray) -> np.ndarray:
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if audio_data.size == 0:
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return audio_data
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audio_int32 = audio_data.astype(np.int32)
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peak = np.max(np.abs(audio_int32))
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if peak <= 0:
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return audio_data
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target_peak = int(32767 * self.peak_target)
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normalized = np.rint(audio_int32 * (target_peak / peak)).astype(np.int32)
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np.clip(normalized, -32768, 32767, out=normalized)
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return normalized.astype(np.int16)
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def compress_pcm16(self, audio_data: np.ndarray, sample_rate: int) -> np.ndarray:
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if audio_data.size == 0:
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return audio_data
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filter_chain = (
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"acompressor=threshold=0.1:ratio=3:attack=10:release=120:"
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"makeup=2.0:link=average:detection=rms,"
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"alimiter=limit=0.98"
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)
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ffmpeg_cmd = [
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"ffmpeg",
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"-hide_banner",
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"-loglevel",
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"error",
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"-f",
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"s16le",
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"-ar",
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str(sample_rate),
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"-ac",
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"1",
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"-i",
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"pipe:0",
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"-af",
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filter_chain,
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"-f",
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"s16le",
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"-acodec",
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"pcm_s16le",
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"pipe:1",
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]
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try:
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result = subprocess.run(
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ffmpeg_cmd,
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input=audio_data.tobytes(),
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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check=True,
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)
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except FileNotFoundError:
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logger.warning("ffmpeg not found; skipping compression")
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return audio_data
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except subprocess.CalledProcessError as exc:
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err = exc.stderr.decode(errors="replace").strip()
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logger.warning("ffmpeg compression failed; using raw audio: %s", err)
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return audio_data
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compressed = np.frombuffer(result.stdout, dtype=np.int16)
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if compressed.size == 0:
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return audio_data
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return compressed.copy()
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def prepare_for_transcription(self, audio_data: np.ndarray) -> np.ndarray:
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return self.pcm16_to_float(audio_data)
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@ -7,11 +7,13 @@ import signal
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import subprocess
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import time
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from pathlib import Path
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from typing import Literal
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import numpy as np
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import sounddevice as sd
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from speechd.audio import VoiceActivityDetector
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from speechd.audio_processor import AudioProcessor
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from speechd.config import Config
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from speechd.transcribe import Transcriber
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@ -23,6 +25,7 @@ class SpeechDaemon:
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self.config = config
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logger.info("Loading VAD model...")
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self.vad = VoiceActivityDetector(sample_rate=config.sample_rate)
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self.audio_processor = AudioProcessor()
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self.transcriber = Transcriber(
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api_key=config.groq_api_key,
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model=config.model,
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@ -35,6 +38,7 @@ class SpeechDaemon:
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self.stream: sd.InputStream | None = None
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self.recording_start_time: float | None = None
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self.timeout_cancelled = False
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self.skip_enter = False
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self.runtime_dir = Path(config.runtime_dir)
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self.indicator_file = self.runtime_dir / "speechd.recording"
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@ -72,13 +76,36 @@ class SpeechDaemon:
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else:
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self._stop_recording()
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def toggle_without_enter(self):
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self.skip_enter = True
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self.toggle()
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def cancel(self):
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if not self.recording:
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self.skip_enter = False
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return
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self._stop_recording(cancel=True)
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def _update_status(self, status: Literal["off", "rec", "trans", "type"]):
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match status:
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case "off":
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try:
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self.indicator_file.unlink(missing_ok=True)
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except Exception:
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pass
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case "rec":
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self.indicator_file.write_text("Recording...")
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case "trans":
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self.indicator_file.write_text("Transcribing...")
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case "type":
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self.indicator_file.write_text("Typing...")
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subprocess.call(["pkill", "-39", "dwmblocks"])
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def _start_recording(self):
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logger.info("Recording started")
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self.frames = []
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self.recording = True
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self.timeout_cancelled = False
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self.recording_start_time = time.monotonic()
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self.indicator_file.touch()
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self.stream = sd.InputStream(
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samplerate=self.config.sample_rate,
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channels=1,
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@ -86,14 +113,13 @@ class SpeechDaemon:
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callback=self._audio_callback,
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)
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self.stream.start()
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self._update_status("rec")
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def _stop_recording(self, timeout: bool = False):
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def _stop_recording(self, timeout: bool = False, cancel: bool = False):
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self.recording = False
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self.recording_start_time = None
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try:
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self.indicator_file.unlink(missing_ok=True)
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except Exception:
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pass
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append_enter = self.skip_enter
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self.skip_enter = False
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if self.stream:
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self.stream.stop()
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@ -102,12 +128,21 @@ class SpeechDaemon:
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if timeout:
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logger.info(f"Recording cancelled: exceeded {self.config.timeout_seconds}s timeout")
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self._update_status("off")
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return
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if cancel:
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logger.info("Recording cancelled")
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self._update_status("off")
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return
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if not self.frames:
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self._update_status("off")
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return
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audio_data = np.concatenate(self.frames)
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audio_data = self.audio_processor.normalize_pcm16_peak(audio_data)
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audio_data = self.audio_processor.compress_pcm16(audio_data, self.config.sample_rate)
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duration = len(audio_data) / self.config.sample_rate
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logger.info(f"Processing {duration:.1f}s of audio...")
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@ -117,8 +152,10 @@ class SpeechDaemon:
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if len(audio_clean) == 0:
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logger.info(f"No speech detected (VAD: {vad_time:.2f}s)")
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self._update_status("off")
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return
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self._update_status("trans")
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logger.info(f"Transcribing... (VAD: {vad_time:.2f}s)")
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t1 = time.monotonic()
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result = self.transcriber.transcribe(audio_clean)
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@ -126,7 +163,9 @@ class SpeechDaemon:
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if result.success and result.text:
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logger.info(f"[{transcribe_time:.2f}s] {result.text}")
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self._type_text(result.text)
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self._update_status("type")
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self._type_text(result.text, skip_enter=append_enter)
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self._update_status("off")
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def _audio_callback(self, indata, _frames, _time, _status):
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if not self.recording:
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@ -148,11 +187,14 @@ class SpeechDaemon:
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text = re.sub(r"(\s)-(\s)", r"\1--\2", text)
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return text
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def _type_text(self, text: str):
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def _type_text(self, text: str, skip_enter: bool = False):
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text = self._postprocess_text(text).strip().replace("\n", " ")
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if text:
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if not text:
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return
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text = text + ('\n' if not skip_enter else '')
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try:
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subprocess.run(["wtype", "-"], input=text.encode(), check=True)
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subprocess.run(["xtype", self.config.language], input=text.encode(), check=True)
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except subprocess.CalledProcessError as e:
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logger.error(f"Failed to type text: {e}")
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except FileNotFoundError:
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@ -164,6 +206,8 @@ class SpeechDaemon:
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raise SystemExit(1)
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signal.signal(signal.SIGUSR1, lambda *_: self.toggle())
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signal.signal(signal.SIGUSR2, lambda *_: self.toggle_without_enter())
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signal.signal(signal.SIGALRM, lambda *_: self.cancel())
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signal.signal(signal.SIGTERM, lambda *_: (self.cleanup(), exit(0)))
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signal.signal(signal.SIGINT, lambda *_: (self.cleanup(), exit(0)))
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atexit.register(self.cleanup)
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@ -6,6 +6,8 @@ import numpy as np
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import soundfile as sf
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from groq import Groq
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from speechd.audio_processor import AudioProcessor
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logger = logging.getLogger(__name__)
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@ -25,6 +27,7 @@ class Transcriber:
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self.language = language
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self.sample_rate = sample_rate
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self.audio_quality = audio_quality
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self.audio_processor = AudioProcessor()
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def transcribe(self, audio_data: np.ndarray) -> TranscriptionResult:
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if len(audio_data) == 0:
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@ -47,16 +50,8 @@ class Transcriber:
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logger.error(f"Transcription failed: {e}")
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return TranscriptionResult(text="", success=False, error=str(e))
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def _normalize_rms(self, audio: np.ndarray, target_rms: float = 0.1) -> np.ndarray:
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rms = np.sqrt(np.mean(audio**2))
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if rms > 1e-8:
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audio = audio * (target_rms / rms)
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np.clip(audio, -1.0, 1.0, out=audio)
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return audio
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def _encode_opus(self, audio_data: np.ndarray) -> bytes:
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audio_float = audio_data.astype(np.float32) / 32768.0
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audio_float = self._normalize_rms(audio_float)
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audio_float = self.audio_processor.prepare_for_transcription(audio_data)
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buf = io.BytesIO()
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sf.write(
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buf,
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13
uv.lock
generated
13
uv.lock
generated
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@ -538,6 +538,15 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/a2/eb/86626c1bbc2edb86323022371c39aa48df6fd8b0a1647bc274577f72e90b/nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5b17e2001cc0d751a5bc2c6ec6d26ad95913324a4adb86788c944f8ce9ba441f", size = 89954, upload-time = "2025-03-07T01:42:44.131Z" },
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]
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[[package]]
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name = "packaging"
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version = "26.0"
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source = { registry = "https://pypi.org/simple" }
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sdist = { url = "https://files.pythonhosted.org/packages/65/ee/299d360cdc32edc7d2cf530f3accf79c4fca01e96ffc950d8a52213bd8e4/packaging-26.0.tar.gz", hash = "sha256:00243ae351a257117b6a241061796684b084ed1c516a08c48a3f7e147a9d80b4", size = 143416, upload-time = "2026-01-21T20:50:39.064Z" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/b7/b9/c538f279a4e237a006a2c98387d081e9eb060d203d8ed34467cc0f0b9b53/packaging-26.0-py3-none-any.whl", hash = "sha256:b36f1fef9334a5588b4166f8bcd26a14e521f2b55e6b9de3aaa80d3ff7a37529", size = 74366, upload-time = "2026-01-21T20:50:37.788Z" },
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]
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[[package]]
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name = "pycparser"
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version = "3.0"
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@ -714,11 +723,12 @@ wheels = [
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[[package]]
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name = "speechd"
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version = "1.1.0"
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version = "1.2.8"
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source = { editable = "." }
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dependencies = [
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{ name = "groq" },
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{ name = "numpy" },
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{ name = "packaging" },
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{ name = "sounddevice" },
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{ name = "soundfile" },
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{ name = "torch" },
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@ -729,6 +739,7 @@ dependencies = [
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requires-dist = [
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{ name = "groq", specifier = ">=0.4.0" },
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{ name = "numpy", specifier = ">=1.24.0" },
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{ name = "packaging", specifier = ">=26.0" },
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{ name = "sounddevice", specifier = ">=0.4.6" },
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{ name = "soundfile", specifier = ">=0.12.0" },
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{ name = "torch", specifier = ">=2.0.0" },
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