pip install moonshine-voice

# Listens to the microphone, logging to the console when there are 
# speech updates.
moonshine-voice mic

"""Transcribes live audio from the default microphone"""
import time
from moonshine_voice import (
    MicTranscriber,
    TranscriptEventListener,
    get_model_for_language,
)

# This will download the model files and cache them.
model_path, model_arch = get_model_for_language("en")

# MicTranscriber handles connecting to the microphone, capturing
# the audio data, detecting voice activity, breaking the speech
# up into segments, transcribing the speech, and sending events
# as the results are updated over time.
mic_transcriber = MicTranscriber(
    model_path=model_path, model_arch=model_arch)

# We use an event-driven interface to respond in real time
# as speech is detected.
class TestListener(TranscriptEventListener):
    def on_line_started(self, event):
        print(f"Line started: {event.line.text}")

    def on_line_text_changed(self, event):
        print(f"Line text changed: {event.line.text}")

    def on_line_completed(self, event):
        print(f"Line completed: {event.line.text}")

listener = TestListener()
mic_transcriber.add_listener(listener)
mic_transcriber.start()
print("Listening to the microphone, press Ctrl+C to stop...")

while True:
    time.sleep(0.1)

"""Transcribes live audio from an arbitrary audio source."""
from moonshine_voice import (
    Transcriber,
    TranscriptEventListener,
    get_model_for_language,
    load_wav_file,
    get_assets_path,
)
import os
from typing import Iterator, Tuple


def audio_chunk_generator(
    wav_file_path: str, chunk_duration: float = 0.1
) -> Iterator[Tuple[list, int]]:
    """
    Example function that loads a WAV file and yields audio chunks.

    This demonstrates how you can integrate your own proprietary
    audio data capture sources. Replace this function with your own
    implementation that yields (audio_chunk, sample_rate) tuples.

    Args:
        wav_file_path: Path to the WAV file to load
        chunk_duration: Duration of each chunk in seconds

    Yields:
        Tuple of (audio_chunk, sample_rate) where:
        - audio_chunk: List of float audio samples
        - sample_rate: Sample rate in Hz
    """
    audio_data, sample_rate = load_wav_file(wav_file_path)
    chunk_size = int(chunk_duration * sample_rate)

    for i in range(0, len(audio_data), chunk_size):
        chunk = audio_data[i: i + chunk_size]
        yield (chunk, sample_rate)


model_path, model_arch = get_model_for_language("en")

transcriber = Transcriber(
    model_path=model_path, model_arch=model_arch)

stream = transcriber.create_stream(update_interval=0.5)
stream.start()


class TestListener(TranscriptEventListener):
    def on_line_started(self, event):
        print(f"{event.line.start_time:.2f}s: Line started: {event.line.text}")

    def on_line_text_changed(self, event):
        print(
            f"{event.line.start_time:.2f}s: Line text changed: {event.line.text}")

    def on_line_completed(self, event):
        print(f"{event.line.start_time:.2f}s: Line completed: {event.line.text}")


listener = TestListener()
stream.add_listener(listener)

# Feed audio chunks from the generator into the stream.
wav_file_path = os.path.join(get_assets_path(), "two_cities.wav")
for chunk, sample_rate in audio_chunk_generator(wav_file_path):
    stream.add_audio(chunk, sample_rate)

stream.stop()
stream.close()

from moonshine_voice import DialogFlow

def lights_on(d):
    print("\n💡 LIGHTS ON!")

def lights_off(d):
    print("\n🌑 LIGHTS OFF!")

runner = (
    DialogFlow()
    .always("turn on the lights", lights_on)
    .always("turn off the lights", lights_off)
)

runner.load()
runner.start_listening()
try:
    while True:
        time.sleep(0.1)
except KeyboardInterrupt:
    print("\n\nStopping...", file=sys.stderr)
finally:
    runner.close()