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Refactor live_captions.py #46

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Refactor live_captions.py #46

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4fc884a
Refactor live_captions.py.
guynich Nov 1, 2024
81c03b4
Apply code formatting
guynich Nov 1, 2024
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53 changes: 24 additions & 29 deletions moonshine/demo/live_captions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,25 +23,23 @@

CHUNK_SIZE = 512 # Silero VAD requirement with sampling rate 16000.
LOOKBACK_CHUNKS = 5
MARKER_LENGTH = 6
MAX_LINE_LENGTH = 80

# These affect live caption updating - adjust for your platform speed and model.
MAX_SPEECH_SECS = 15
MIN_REFRESH_SECS = 0.2

VERBOSE = False


class Transcriber(object):
def __init__(self, model_name, rate=16000):
if rate != 16000:
raise ValueError("Moonshine supports sampling rate 16000 Hz.")
self.model = MoonshineOnnxModel(model_name=model_name)
self.rate = rate
assets_dir = f"{os.path.join(os.path.dirname(__file__), '..', 'assets')}"
tokenizer_file = f"{assets_dir}{os.sep}tokenizer.json"
self.tokenizer = Tokenizer.from_file(str(tokenizer_file))
tokenizer_path = os.path.join(
MOONSHINE_DEMO_DIR, "..", "assets", "tokenizer.json"
)
self.tokenizer = Tokenizer.from_file(tokenizer_path)

self.inference_secs = 0
self.number_inferences = 0
Expand Down Expand Up @@ -72,32 +70,35 @@ def input_callback(data, frames, time, status):
return input_callback


def end_recording(speech, marker=""):
def end_recording(speech):
"""Transcribes, caches and prints the caption. Clears speech buffer."""
if len(marker) != MARKER_LENGTH:
raise ValueError("Unexpected marker length.")
text = transcribe(speech)
caption_cache.append(text + " " + marker)
print_captions(text + (" " + marker) if VERBOSE else "", True)
caption_cache.append(text)
print_captions(text, new_cached_caption=True)
speech *= 0.0


def print_captions(text, new_cached_caption=False):
"""Prints right justified on same line, prepending cached captions."""
print("\r" + " " * MAX_LINE_LENGTH, end="", flush=True)
if len(text) > MAX_LINE_LENGTH:
text = text[-MAX_LINE_LENGTH:]
elif text != "\n":
if len(text) < MAX_LINE_LENGTH:
for caption in caption_cache[::-1]:
text = (caption[:-MARKER_LENGTH] if not VERBOSE else caption + " ") + text
text = caption + " " + text
if len(text) > MAX_LINE_LENGTH:
break
if len(text) > MAX_LINE_LENGTH:
text = text[-MAX_LINE_LENGTH:]
if len(text) > MAX_LINE_LENGTH:
text = text[-MAX_LINE_LENGTH:]
text = " " * (MAX_LINE_LENGTH - len(text)) + text
print("\r" + text, end="", flush=True)


def soft_reset(vad_iterator):
"""Soft resets Silero VADIterator without affecting VAD model state."""
vad_iterator.triggered = False
vad_iterator.temp_end = 0
vad_iterator.current_sample = 0


if __name__ == "__main__":
parser = argparse.ArgumentParser(
prog="live_captions",
Expand Down Expand Up @@ -145,7 +146,7 @@ def print_captions(text, new_cached_caption=False):
try:
while True:
chunk, status = q.get()
if VERBOSE and status:
if status:
print(status)

speech = np.concatenate((speech, chunk))
Expand All @@ -160,18 +161,15 @@ def print_captions(text, new_cached_caption=False):

if "end" in speech_dict and recording:
recording = False
end_recording(speech, "<STOP>")
end_recording(speech)

elif recording:
# Possible speech truncation can cause hallucination.

if (len(speech) / SAMPLING_RATE) > MAX_SPEECH_SECS:
recording = False
end_recording(speech, "<SNIP>")
# Soft reset without affecting VAD model state.
vad_iterator.triggered = False
vad_iterator.temp_end = 0
vad_iterator.current_sample = 0
end_recording(speech)
soft_reset(vad_iterator)

if (time.time() - start_time) > MIN_REFRESH_SECS:
print_captions(transcribe(speech))
Expand All @@ -184,7 +182,7 @@ def print_captions(text, new_cached_caption=False):
while not q.empty():
chunk, _ = q.get()
speech = np.concatenate((speech, chunk))
end_recording(speech, "<END.>")
end_recording(speech)

print(f"""

Expand All @@ -196,7 +194,4 @@ def print_captions(text, new_cached_caption=False):
model realtime factor : {(transcribe.speech_secs / transcribe.inference_secs):0.2f}x
""")
if caption_cache:
print("Cached captions.")
for caption in caption_cache:
print(caption[:-MARKER_LENGTH], end="", flush=True)
print("")
print(f"Cached captions.\n{' '.join(caption_cache)}")