refactor: restructure project into src layout with proper packaging
Split monolithic transcribe.py into focused modules under src/cohere_transcribe/ (model, vad, stream, cli), move tests into tests/, add hatchling build system and CLI entry point, remove unused shell.nix and main.py. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
@@ -8,3 +8,7 @@ wheels/
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# Virtual environments
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.venv
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# Nix
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.direnv/
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result
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@@ -1,6 +0,0 @@
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def main():
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print("Hello from cohere!")
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if __name__ == "__main__":
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main()
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+12
-2
@@ -1,7 +1,7 @@
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[project]
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name = "cohere"
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name = "cohere-transcribe"
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version = "0.1.0"
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description = "Add your description here"
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description = "Live speech transcription using Cohere ASR"
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readme = "README.md"
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requires-python = ">=3.14"
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dependencies = [
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@@ -15,3 +15,13 @@ dependencies = [
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"torch>=2.12.0",
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"transformers>=5.9.0",
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]
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[project.scripts]
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cohere-transcribe = "cohere_transcribe.cli:main"
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.backends"
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[tool.hatch.build.targets.wheel]
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packages = ["src/cohere_transcribe"]
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@@ -1,15 +0,0 @@
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{ pkgs ? import <nixpkgs> { config.allowUnfree = true; } }:
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pkgs.mkShell {
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buildInputs = with pkgs; [
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portaudio
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cudaPackages.cudatoolkit
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uv
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python314
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];
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shellHook = ''
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export LD_LIBRARY_PATH="${pkgs.cudaPackages.cudatoolkit}/lib:$LD_LIBRARY_PATH"
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echo "Dev shell ready - microphone input enabled"
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'';
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}
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@@ -0,0 +1,40 @@
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import argparse
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from huggingface_hub import hf_hub_download
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from transformers.audio_utils import load_audio
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from .model import MODEL_ID, SAMPLE_RATE, load_model, record_audio, transcribe_audio
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from .stream import stream_transcribe
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def main():
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parser = argparse.ArgumentParser(description="Cohere ASR Transcription")
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group = parser.add_mutually_exclusive_group()
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group.add_argument("--mic", type=int, nargs="?", const=5, metavar="SECONDS",
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help="Record from microphone for N seconds (default: 5)")
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group.add_argument("--stream", action="store_true",
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help="Live streaming transcription with VAD")
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parser.add_argument("--lang", default="en", help="Language code (default: en)")
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args = parser.parse_args()
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if args.stream:
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processor, model = load_model()
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stream_transcribe(processor, model, args.lang)
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elif args.mic is not None:
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processor, model = load_model()
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try:
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mic_audio = record_audio(args.mic)
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print("Transcribing...")
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text = transcribe_audio(processor, model, mic_audio, args.lang)
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print(f"\nTranscription:\n{text}\n")
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except OSError as e:
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print(f"Microphone error: {e}")
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print("Hint: Run with nix-shell for PortAudio support")
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else:
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processor, model = load_model()
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print("Loading demo audio...")
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audio_file = hf_hub_download(repo_id=MODEL_ID, filename="demo/voxpopuli_test_en_demo.wav")
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audio = load_audio(audio_file, sampling_rate=SAMPLE_RATE)
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print("Transcribing...")
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text = transcribe_audio(processor, model, audio, args.lang)
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print(f"\nTranscription:\n{text}\n")
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@@ -0,0 +1,32 @@
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import numpy as np
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from transformers import AutoProcessor, CohereAsrForConditionalGeneration
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from transformers.audio_utils import load_audio
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MODEL_ID = "CohereLabs/cohere-transcribe-03-2026"
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SAMPLE_RATE = 16000
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def load_model():
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print("Loading model...")
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processor = AutoProcessor.from_pretrained(MODEL_ID)
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model = CohereAsrForConditionalGeneration.from_pretrained(
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MODEL_ID, device_map="auto"
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)
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return processor, model
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def transcribe_audio(processor, model, audio, language="en"):
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inputs = processor(audio, sampling_rate=SAMPLE_RATE, return_tensors="pt", language=language)
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inputs.to(model.device, dtype=model.dtype)
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outputs = model.generate(**inputs, max_new_tokens=256)
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texts = processor.decode(outputs, skip_special_tokens=True)
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return " ".join(texts) if isinstance(texts, list) else texts
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def record_audio(duration):
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import sounddevice as sd
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print(f"Recording for {duration} seconds...")
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audio = sd.rec(int(duration * SAMPLE_RATE), samplerate=SAMPLE_RATE, channels=1, dtype="float32")
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sd.wait()
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return audio.flatten()
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@@ -0,0 +1,64 @@
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import sys
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import queue
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import threading
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import time
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import numpy as np
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import sounddevice as sd
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from .model import SAMPLE_RATE, transcribe_audio
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from .vad import FRAME_SIZE, VADStateMachine, calibrate_silence
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def stream_transcribe(processor, model, language):
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threshold = calibrate_silence()
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vad = VADStateMachine(threshold)
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seg_queue = queue.Queue()
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stop_event = threading.Event()
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start_time = time.monotonic()
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def transcription_worker():
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while not stop_event.is_set() or not seg_queue.empty():
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try:
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seg_start, audio = seg_queue.get(timeout=0.5)
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except queue.Empty:
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continue
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minutes = int(seg_start) // 60
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seconds = int(seg_start) % 60
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text = transcribe_audio(processor, model, audio, language)
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if text.strip():
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print(f"[{minutes:02d}:{seconds:02d}] {text.strip()}")
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worker = threading.Thread(target=transcription_worker, daemon=True)
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worker.start()
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def audio_callback(indata, frames, time_info, status):
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if stop_event.is_set():
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return
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elapsed = time.monotonic() - start_time
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result = vad.process_frame(indata[:, 0].copy(), elapsed)
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if result is not None:
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seg_queue.put(result)
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print("Listening... (Ctrl+C to stop)")
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stream = sd.InputStream(
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samplerate=SAMPLE_RATE, channels=1, dtype="float32",
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callback=audio_callback, blocksize=FRAME_SIZE,
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)
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try:
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with stream:
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while True:
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time.sleep(0.1)
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except KeyboardInterrupt:
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pass
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stop_event.set()
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if vad.speaking and vad.segment:
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seg_queue.put((vad.segment_start_time, np.concatenate(vad.segment)))
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worker.join(timeout=30)
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if worker.is_alive():
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print("Warning: transcription worker did not finish in time.", file=sys.stderr)
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print("\nDone.")
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@@ -0,0 +1,73 @@
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import collections
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import numpy as np
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import sounddevice as sd
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from .model import SAMPLE_RATE
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FRAME_SIZE = 800 # 50ms at 16kHz
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PRE_ROLL_FRAMES = 6 # ~0.3s of audio before speech onset
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SILENCE_FRAMES = 16 # ~0.8s of silence to end a segment
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SPEECH_ONSET_FRAMES = 3 # ~150ms of speech to trigger
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MAX_SPEECH_SECONDS = 30 # force chunk boundary
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def calibrate_silence(duration=0.5):
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print("Calibrating silence threshold...")
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audio = sd.rec(int(duration * SAMPLE_RATE), samplerate=SAMPLE_RATE, channels=1, dtype="float32")
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sd.wait()
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rms = np.sqrt(np.mean(audio ** 2))
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threshold = max(rms * 3, 0.01)
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print(f" Ambient RMS: {rms:.4f}, threshold: {threshold:.4f}")
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return threshold
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class VADStateMachine:
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def __init__(self, threshold):
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self.threshold = threshold
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self.speaking = False
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self.speech_frames = 0
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self.silence_frames = 0
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self.pre_roll = collections.deque(maxlen=PRE_ROLL_FRAMES)
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self.segment = []
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self.segment_start_time = 0.0
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def process_frame(self, frame, elapsed_time):
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"""Process one 50ms frame. Returns a (start_time, audio_array) tuple when a
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complete speech segment is detected, otherwise None."""
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rms = np.sqrt(np.mean(frame ** 2))
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is_loud = rms > self.threshold
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if not self.speaking:
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self.pre_roll.append(frame)
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if is_loud:
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self.speech_frames += 1
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if self.speech_frames >= SPEECH_ONSET_FRAMES:
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self.speaking = True
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self.silence_frames = 0
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self.segment = list(self.pre_roll)
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self.segment_start_time = max(0.0, elapsed_time - len(self.pre_roll) * FRAME_SIZE / SAMPLE_RATE)
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self.pre_roll = collections.deque(maxlen=PRE_ROLL_FRAMES)
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else:
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self.speech_frames = 0
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return None
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self.segment.append(frame)
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if is_loud:
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self.silence_frames = 0
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else:
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self.silence_frames += 1
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segment_duration = len(self.segment) * FRAME_SIZE / SAMPLE_RATE
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if self.silence_frames >= SILENCE_FRAMES or segment_duration >= MAX_SPEECH_SECONDS:
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result = (self.segment_start_time, np.concatenate(self.segment))
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self.speaking = False
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self.speech_frames = 0
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self.silence_frames = 0
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self.segment = []
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self.pre_roll = collections.deque(maxlen=PRE_ROLL_FRAMES)
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return result
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return None
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-200
@@ -1,200 +0,0 @@
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import sys
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import argparse
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import collections
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import queue
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import threading
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import time
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import numpy as np
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import sounddevice as sd
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from transformers import AutoProcessor, CohereAsrForConditionalGeneration
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from transformers.audio_utils import load_audio
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from huggingface_hub import hf_hub_download
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MODEL_ID = "CohereLabs/cohere-transcribe-03-2026"
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SAMPLE_RATE = 16000
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def load_model():
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print("Loading model...")
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processor = AutoProcessor.from_pretrained(MODEL_ID)
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model = CohereAsrForConditionalGeneration.from_pretrained(
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MODEL_ID, device_map="auto"
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)
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return processor, model
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def transcribe_audio(processor, model, audio, language="en"):
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inputs = processor(audio, sampling_rate=SAMPLE_RATE, return_tensors="pt", language=language)
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inputs.to(model.device, dtype=model.dtype)
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outputs = model.generate(**inputs, max_new_tokens=256)
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texts = processor.decode(outputs, skip_special_tokens=True)
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return " ".join(texts) if isinstance(texts, list) else texts
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def record_audio(duration):
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print(f"Recording for {duration} seconds...")
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audio = sd.rec(int(duration * SAMPLE_RATE), samplerate=SAMPLE_RATE, channels=1, dtype="float32")
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sd.wait()
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return audio.flatten()
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def main():
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parser = argparse.ArgumentParser(description="Cohere ASR Transcription")
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group = parser.add_mutually_exclusive_group()
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group.add_argument("--mic", type=int, nargs="?", const=5, metavar="SECONDS",
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help="Record from microphone for N seconds (default: 5)")
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group.add_argument("--stream", action="store_true",
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help="Live streaming transcription with VAD")
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parser.add_argument("--lang", default="en", help="Language code (default: en)")
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args = parser.parse_args()
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if args.stream:
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processor, model = load_model()
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stream_transcribe(processor, model, args.lang)
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elif args.mic is not None:
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processor, model = load_model()
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try:
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mic_audio = record_audio(args.mic)
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print("Transcribing...")
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text = transcribe_audio(processor, model, mic_audio, args.lang)
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print(f"\nTranscription:\n{text}\n")
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except OSError as e:
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print(f"Microphone error: {e}")
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print("Hint: Run with nix-shell for PortAudio support")
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else:
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processor, model = load_model()
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print("Loading demo audio...")
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audio_file = hf_hub_download(repo_id=MODEL_ID, filename="demo/voxpopuli_test_en_demo.wav")
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audio = load_audio(audio_file, sampling_rate=SAMPLE_RATE)
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print("Transcribing...")
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text = transcribe_audio(processor, model, audio, args.lang)
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print(f"\nTranscription:\n{text}\n")
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def calibrate_silence(duration=0.5):
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print("Calibrating silence threshold...")
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audio = sd.rec(int(duration * SAMPLE_RATE), samplerate=SAMPLE_RATE, channels=1, dtype="float32")
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sd.wait()
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rms = np.sqrt(np.mean(audio ** 2))
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threshold = max(rms * 3, 0.01)
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print(f" Ambient RMS: {rms:.4f}, threshold: {threshold:.4f}")
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return threshold
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FRAME_SIZE = 800 # 50ms at 16kHz
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PRE_ROLL_FRAMES = 6 # ~0.3s of audio before speech onset
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SILENCE_FRAMES = 16 # ~0.8s of silence to end a segment
|
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SPEECH_ONSET_FRAMES = 3 # ~150ms of speech to trigger
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MAX_SPEECH_SECONDS = 30 # force chunk boundary
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class VADStateMachine:
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def __init__(self, threshold):
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self.threshold = threshold
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self.speaking = False
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self.speech_frames = 0
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self.silence_frames = 0
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self.pre_roll = collections.deque(maxlen=PRE_ROLL_FRAMES)
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self.segment = []
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self.segment_start_time = 0.0
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|
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def process_frame(self, frame, elapsed_time):
|
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"""Process one 50ms frame. Returns a (start_time, audio_array) tuple when a
|
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complete speech segment is detected, otherwise None."""
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rms = np.sqrt(np.mean(frame ** 2))
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is_loud = rms > self.threshold
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|
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if not self.speaking:
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self.pre_roll.append(frame)
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if is_loud:
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self.speech_frames += 1
|
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if self.speech_frames >= SPEECH_ONSET_FRAMES:
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self.speaking = True
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self.silence_frames = 0
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self.segment = list(self.pre_roll)
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self.segment_start_time = max(0.0, elapsed_time - len(self.pre_roll) * FRAME_SIZE / SAMPLE_RATE)
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self.pre_roll = collections.deque(maxlen=PRE_ROLL_FRAMES)
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else:
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self.speech_frames = 0
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return None
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# Currently speaking
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self.segment.append(frame)
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if is_loud:
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self.silence_frames = 0
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else:
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self.silence_frames += 1
|
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segment_duration = len(self.segment) * FRAME_SIZE / SAMPLE_RATE
|
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if self.silence_frames >= SILENCE_FRAMES or segment_duration >= MAX_SPEECH_SECONDS:
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result = (self.segment_start_time, np.concatenate(self.segment))
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self.speaking = False
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self.speech_frames = 0
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self.silence_frames = 0
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self.segment = []
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self.pre_roll = collections.deque(maxlen=PRE_ROLL_FRAMES)
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return result
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return None
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def stream_transcribe(processor, model, language):
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threshold = calibrate_silence()
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vad = VADStateMachine(threshold)
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seg_queue = queue.Queue()
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stop_event = threading.Event()
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start_time = time.monotonic()
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def transcription_worker():
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while not stop_event.is_set() or not seg_queue.empty():
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try:
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seg_start, audio = seg_queue.get(timeout=0.5)
|
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except queue.Empty:
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continue
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minutes = int(seg_start) // 60
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seconds = int(seg_start) % 60
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text = transcribe_audio(processor, model, audio, language)
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if text.strip():
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print(f"[{minutes:02d}:{seconds:02d}] {text.strip()}")
|
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|
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worker = threading.Thread(target=transcription_worker, daemon=True)
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worker.start()
|
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|
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def audio_callback(indata, frames, time_info, status):
|
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if stop_event.is_set():
|
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return
|
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elapsed = time.monotonic() - start_time
|
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result = vad.process_frame(indata[:, 0].copy(), elapsed)
|
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if result is not None:
|
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seg_queue.put(result)
|
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|
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print("Listening... (Ctrl+C to stop)")
|
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stream = sd.InputStream(
|
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samplerate=SAMPLE_RATE, channels=1, dtype="float32",
|
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callback=audio_callback, blocksize=FRAME_SIZE,
|
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)
|
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|
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try:
|
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with stream:
|
||||
while True:
|
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time.sleep(0.1)
|
||||
except KeyboardInterrupt:
|
||||
pass
|
||||
|
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stop_event.set()
|
||||
|
||||
# Flush any remaining speech segment
|
||||
if vad.speaking and vad.segment:
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elapsed = time.monotonic() - start_time
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seg_queue.put((vad.segment_start_time, np.concatenate(vad.segment)))
|
||||
|
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worker.join(timeout=30)
|
||||
if worker.is_alive():
|
||||
print("Warning: transcription worker did not finish in time.", file=sys.stderr)
|
||||
print("\nDone.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user