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added : task, word_timestamps, without_timestamps , and for realtime change try from outside to inside loop #168

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added : task, word_timestamps, without_timestamps , and for realtime change try from outside to inside loop #168

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f8a4df8
added : task, word_timestamps, without_timestamps
TommasoRella-github Dec 15, 2024
5428f86
Update audio_recorder.py
TommasoRella-github Dec 15, 2024
fcd4e1e
Update audio_recorder.py
TommasoRella-github Dec 15, 2024
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94 changes: 67 additions & 27 deletions RealtimeSTT/audio_recorder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -57,6 +57,8 @@
import os
import re
import gc
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'

# Set OpenMP runtime duplicate library handling to OK (Use only for development!)
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
Expand Down Expand Up @@ -86,10 +88,10 @@
if platform.system() != 'Darwin':
INIT_HANDLE_BUFFER_OVERFLOW = True


class TranscriptionWorker:
def __init__(self, conn, stdout_pipe, model_path, download_root, compute_type, gpu_device_index, device,
ready_event, shutdown_event, interrupt_stop_event, beam_size, initial_prompt, suppress_tokens, batch_size):
ready_event, shutdown_event, interrupt_stop_event, beam_size, initial_prompt, suppress_tokens, batch_size,
task, word_timestamps, without_timestamps):
self.conn = conn
self.stdout_pipe = stdout_pipe
self.model_path = model_path
Expand All @@ -104,7 +106,10 @@ def __init__(self, conn, stdout_pipe, model_path, download_root, compute_type, g
self.initial_prompt = initial_prompt
self.suppress_tokens = suppress_tokens
self.batch_size = batch_size
self.queue = queue.Queue()
self.queue = queue.Queue()
self.task = task
self.without_timestamps = without_timestamps
self.word_timestamps = word_timestamps

def custom_print(self, *args, **kwargs):
message = ' '.join(map(str, args))
Expand All @@ -131,16 +136,18 @@ def run(self):

logging.info(f"Initializing faster_whisper main transcription model {self.model_path}")


try:
model = faster_whisper.WhisperModel(
model_size_or_path=self.model_path,
device=self.device,
device=self.device,###'cuda' if torch.cuda.is_available() else 'cpu',
compute_type=self.compute_type,
device_index=self.gpu_device_index,
download_root=self.download_root,
)
if self.batch_size > 0:
model = BatchedInferencePipeline(model=model)

except Exception as e:
logging.exception(f"Error initializing main faster_whisper transcription model: {e}")
raise
Expand All @@ -164,17 +171,29 @@ def run(self):
beam_size=self.beam_size,
initial_prompt=self.initial_prompt,
suppress_tokens=self.suppress_tokens,
batch_size=self.batch_size
batch_size=self.batch_size,
task=self.task,
without_timestamps=self.without_timestamps,
word_timestamps=self.word_timestamps
)
else:
segments, info = model.transcribe(
audio,
language=language if language else None,
beam_size=self.beam_size,
initial_prompt=self.initial_prompt,
suppress_tokens=self.suppress_tokens
suppress_tokens=self.suppress_tokens,
task=self.task,
without_timestamps=self.without_timestamps,
word_timestamps=self.word_timestamps
)

# # Print transcription and translation segments
#print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
# for segment in segments:
# print("\n[%.2fs - %.2fs] %s" % (segment.start, segment.end, segment.text))
# time.sleep(10)
#segments = segments[0]
transcription = " ".join(seg.text for seg in segments).strip()
logging.debug(f"Final text detected with main model: {transcription}")
self.conn.send(('success', (transcription, info)))
Expand Down Expand Up @@ -259,7 +278,7 @@ def __init__(self,
on_vad_detect_stop=None,

# Wake word parameters
wakeword_backend: str = "pvporcupine",
wakeword_backend: str = "pvporcupine", #"oww", "pvporcupine"
openwakeword_model_paths: str = None,
openwakeword_inference_framework: str = "onnx",
wake_words: str = "",
Expand All @@ -276,8 +295,8 @@ def __init__(self,
on_recorded_chunk=None,
debug_mode=False,
handle_buffer_overflow: bool = INIT_HANDLE_BUFFER_OVERFLOW,
beam_size: int = 5,
beam_size_realtime: int = 3,
beam_size: int = 5, #<---how many searching path to use.(https://en.wikipedia.org/wiki/Beam_search) (1=faster,lessContextPrecision ...5=lower,more context precision) (1-deep search in 1 branch, 5 -deep search in 5 branchs simultan.)
beam_size_realtime: int = 3, #<---how many searching path to use.(https://en.wikipedia.org/wiki/Beam_search) (1=faster,lessContextPrecision ...5=lower,more context precision) (1-deep search in 1 branch, 5 -deep search in 5 branchs simultan.)
buffer_size: int = BUFFER_SIZE,
sample_rate: int = SAMPLE_RATE,
initial_prompt: Optional[Union[str, Iterable[int]]] = None,
Expand All @@ -287,7 +306,11 @@ def __init__(self,
allowed_latency_limit: int = ALLOWED_LATENCY_LIMIT,
no_log_file: bool = False,
use_extended_logging: bool = False,
task: str = "transcribe",
without_timestamps: int = False,
word_timestamps: int = True,
):
print( "AudioToTextRecorder___INIT___batched_model" )
"""
Initializes an audio recorder and transcription
and wake word detection.
Expand Down Expand Up @@ -494,7 +517,9 @@ def __init__(self,
- use_extended_logging (bool, default=False): Writes extensive
log messages for the recording worker, that processes the audio
chunks.

- task "translate" or "transcribe"
- without_timestamps: int = False,
- word_timestamps: int = True,
Raises:
Exception: Errors related to initializing transcription
model, wake word detection, or audio recording.
Expand Down Expand Up @@ -595,7 +620,9 @@ def __init__(self,
self.print_transcription_time = print_transcription_time
self.early_transcription_on_silence = early_transcription_on_silence
self.use_extended_logging = use_extended_logging

self.task = task
self.without_timestamps = without_timestamps
self.word_timestamps = word_timestamps
# Initialize the logging configuration with the specified level
log_format = 'RealTimeSTT: %(name)s - %(levelname)s - %(message)s'

Expand Down Expand Up @@ -669,7 +696,10 @@ def __init__(self,
self.beam_size,
self.initial_prompt,
self.suppress_tokens,
self.batch_size
self.batch_size,
self.task,
self.without_timestamps,
self.word_timestamps ,
)
)

Expand Down Expand Up @@ -824,7 +854,6 @@ def __init__(self,
verbose=False,
onnx=silero_use_onnx
)

except Exception as e:
logging.exception(f"Error initializing Silero VAD "
f"voice activity detection engine: {e}"
Expand Down Expand Up @@ -2045,18 +2074,18 @@ def _realtime_worker(self):
and a callback
function is invoked with this text if specified.
"""
### better inside the loop
# try:

try:

logging.debug('Starting realtime worker')

# Return immediately if real-time transcription is not enabled
if not self.enable_realtime_transcription:
return
logging.debug('Starting realtime worker')

# Continue running as long as the main process is active
while self.is_running:
# Return immediately if real-time transcription is not enabled
if not self.enable_realtime_transcription:
return

# Continue running as long as the main process is active
while self.is_running:
try:
# Check if the recording is active
if self.is_recording:

Expand Down Expand Up @@ -2106,15 +2135,21 @@ def _realtime_worker(self):
beam_size=self.beam_size_realtime,
initial_prompt=self.initial_prompt,
suppress_tokens=self.suppress_tokens,
batch_size=self.realtime_batch_size
batch_size=self.realtime_batch_size,
task=self.task,
without_timestamps=self.without_timestamps,
word_timestamps=self.word_timestamps
)
else:
segments, info = self.realtime_model_type.transcribe(
audio_array,
language=self.language if self.language else None,
beam_size=self.beam_size_realtime,
initial_prompt=self.initial_prompt,
suppress_tokens=self.suppress_tokens
suppress_tokens=self.suppress_tokens,
task=self.task,
without_timestamps=self.without_timestamps,
word_timestamps=self.word_timestamps
)

self.detected_realtime_language = info.language if info.language_probability > 0 else None
Expand Down Expand Up @@ -2207,10 +2242,14 @@ def _realtime_worker(self):
# If not recording, sleep briefly before checking again
else:
time.sleep(TIME_SLEEP)
except Exception as e:
logging.error("")

except Exception as e:
logging.error(f"Unhandled exeption in _realtime_worker: {e}")
raise
# except Exception as e:
# logging.error(f"Unhandled exeption in _realtime_worker: {e}")
# raise



def _is_silero_speech(self, chunk):
"""
Expand Down Expand Up @@ -2555,3 +2594,4 @@ def __exit__(self, exc_type, exc_value, traceback):
exception, if any.
"""
self.shutdown()