fix for stt-server (got broken by webserver update)

Author: KoljaB

RealtimeSTT/audio_recorder.py

@@ -139,8 +139,13 @@ def run(self):
                 device_index=self.gpu_device_index,
                 download_root=self.download_root,
             )
+            # Create a short dummy audio array, for example 1 second of silence at 16 kHz
             if self.batch_size > 0:
                 model = BatchedInferencePipeline(model=model)
+
+            # Run a warm-up transcription
+            dummy_audio = np.zeros(16000, dtype=np.float32)
+            model.transcribe(dummy_audio, language="en", beam_size=1)
         except Exception as e:
             logging.exception(f"Error initializing main faster_whisper transcription model: {e}")
             raise
@@ -281,6 +286,7 @@ def __init__(self,
                  buffer_size: int = BUFFER_SIZE,
                  sample_rate: int = SAMPLE_RATE,
                  initial_prompt: Optional[Union[str, Iterable[int]]] = None,
+                 initial_prompt_realtime: Optional[Union[str, Iterable[int]]] = None,
                  suppress_tokens: Optional[List[int]] = [-1],
                  print_transcription_time: bool = False,
                  early_transcription_on_silence: int = 0,
@@ -294,12 +300,12 @@ def __init__(self,
 
         Args:
         - model (str, default="tiny"): Specifies the size of the transcription
-          model to use or the path to a converted model directory.
-                Valid options are 'tiny', 'tiny.en', 'base', 'base.en',
-                'small', 'small.en', 'medium', 'medium.en', 'large-v1',
-                'large-v2'.
-                If a specific size is provided, the model is downloaded
-                from the Hugging Face Hub.
+            model to use or the path to a converted model directory.
+            Valid options are 'tiny', 'tiny.en', 'base', 'base.en',
+            'small', 'small.en', 'medium', 'medium.en', 'large-v1',
+            'large-v2'.
+            If a specific size is provided, the model is downloaded
+            from the Hugging Face Hub.
         - download_root (str, default=None): Specifies the root path were the Whisper models 
           are downloaded to. When empty, the default is used. 
         - language (str, default=""): Language code for speech-to-text engine.
@@ -472,7 +478,9 @@ def __init__(self,
             recording. Changing this will very probably functionality (as the
             WebRTC VAD model is very sensitive towards the sample rate).
         - initial_prompt (str or iterable of int, default=None): Initial
-            prompt to be fed to the transcription models.
+            prompt to be fed to the main transcription model.
+        - initial_prompt_realtime (str or iterable of int, default=None):
+            Initial prompt to be fed to the real-time transcription model.
         - suppress_tokens (list of int, default=[-1]): Tokens to be suppressed
             from the transcription output.
         - print_transcription_time (bool, default=False): Logs processing time
@@ -533,6 +541,8 @@ def __init__(self,
         self.enable_realtime_transcription = enable_realtime_transcription
         self.use_main_model_for_realtime = use_main_model_for_realtime
         self.main_model_type = model
+        if not download_root:
+            download_root = None
         self.download_root = download_root
         self.realtime_model_type = realtime_model_type
         self.realtime_processing_pause = realtime_processing_pause
@@ -583,6 +593,7 @@ def __init__(self,
         self.last_transcription_bytes = None
         self.last_transcription_bytes_b64 = None
         self.initial_prompt = initial_prompt
+        self.initial_prompt_realtime = initial_prompt_realtime
         self.suppress_tokens = suppress_tokens
         self.use_wake_words = wake_words or wakeword_backend in {'oww', 'openwakeword', 'openwakewords'}
         self.detected_language = None
@@ -697,7 +708,11 @@ def __init__(self,
         if self.enable_realtime_transcription and not self.use_main_model_for_realtime:
             try:
                 logging.info("Initializing faster_whisper realtime "
-                             f"transcription model {self.realtime_model_type}"
+                             f"transcription model {self.realtime_model_type}, "
+                             f"default device: {self.device}, "
+                             f"compute type: {self.compute_type}, "
+                             f"device index: {self.gpu_device_index}, "
+                             f"download root: {self.download_root}"
                              )
                 self.realtime_model_type = faster_whisper.WhisperModel(
                     model_size_or_path=self.realtime_model_type,
@@ -708,7 +723,8 @@ def __init__(self,
                 )
                 if self.realtime_batch_size > 0:
                     self.realtime_model_type = BatchedInferencePipeline(model=self.realtime_model_type)
-
+                dummy_audio = np.zeros(16000, dtype=np.float32)
+                self.realtime_model_type.transcribe(dummy_audio, language="en", beam_size=1)
             except Exception as e:
                 logging.exception("Error initializing faster_whisper "
                                   f"realtime transcription model: {e}"
@@ -2104,7 +2120,7 @@ def _realtime_worker(self):
                                 audio_array,
                                 language=self.language if self.language else None,
                                 beam_size=self.beam_size_realtime,
-                                initial_prompt=self.initial_prompt,
+                                initial_prompt=self.initial_prompt_realtime,
                                 suppress_tokens=self.suppress_tokens,
                                 batch_size=self.realtime_batch_size
                             )
@@ -2113,7 +2129,7 @@ def _realtime_worker(self):
                                 audio_array,
                                 language=self.language if self.language else None,
                                 beam_size=self.beam_size_realtime,
-                                initial_prompt=self.initial_prompt,
+                                initial_prompt=self.initial_prompt_realtime,
                                 suppress_tokens=self.suppress_tokens
                             )
 

RealtimeSTT/audio_recorder_client.py

@@ -28,6 +28,7 @@
 INIT_MODEL_TRANSCRIPTION = "tiny"
 INIT_MODEL_TRANSCRIPTION_REALTIME = "tiny"
 INIT_REALTIME_PROCESSING_PAUSE = 0.2
+INIT_REALTIME_INITIAL_PAUSE = 0.2
 INIT_SILERO_SENSITIVITY = 0.4
 INIT_WEBRTC_SENSITIVITY = 3
 INIT_POST_SPEECH_SILENCE_DURATION = 0.6
@@ -68,6 +69,7 @@ class AudioToTextRecorderClient:
 
     def __init__(self,
                  model: str = INIT_MODEL_TRANSCRIPTION,
+                 download_root: str = None, 
                  language: str = "",
                  compute_type: str = "default",
                  input_device_index: int = None,
@@ -81,14 +83,17 @@ def __init__(self,
                  use_microphone=True,
                  spinner=True,
                  level=logging.WARNING,
+                 batch_size: int = 16,
 
                  # Realtime transcription parameters
                  enable_realtime_transcription=False,
                  use_main_model_for_realtime=False,
                  realtime_model_type=INIT_MODEL_TRANSCRIPTION_REALTIME,
                  realtime_processing_pause=INIT_REALTIME_PROCESSING_PAUSE,
+                 init_realtime_after_seconds=INIT_REALTIME_INITIAL_PAUSE,
                  on_realtime_transcription_update=None,
                  on_realtime_transcription_stabilized=None,
+                 realtime_batch_size: int = 16,
 
                  # Voice activation parameters
                  silero_sensitivity: float = INIT_SILERO_SENSITIVITY,
@@ -133,6 +138,7 @@ def __init__(self,
                  buffer_size: int = BUFFER_SIZE,
                  sample_rate: int = SAMPLE_RATE,
                  initial_prompt: Optional[Union[str, Iterable[int]]] = None,
+                 initial_prompt_realtime: Optional[Union[str, Iterable[int]]] = None,
                  suppress_tokens: Optional[List[int]] = [-1],
                  print_transcription_time: bool = False,
                  early_transcription_on_silence: int = 0,
@@ -162,10 +168,14 @@ def __init__(self,
         self.use_microphone = use_microphone
         self.spinner = spinner
         self.level = level
+        self.batch_size = batch_size
+        self.init_realtime_after_seconds = init_realtime_after_seconds
+        self.realtime_batch_size = realtime_batch_size
 
         # Real-time transcription parameters
         self.enable_realtime_transcription = enable_realtime_transcription
         self.use_main_model_for_realtime = use_main_model_for_realtime
+        self.download_root = download_root
         self.realtime_model_type = realtime_model_type
         self.realtime_processing_pause = realtime_processing_pause
         self.on_realtime_transcription_update = on_realtime_transcription_update
@@ -204,6 +214,7 @@ def __init__(self,
         self.buffer_size = buffer_size
         self.sample_rate = sample_rate
         self.initial_prompt = initial_prompt
+        self.initial_prompt_realtime = initial_prompt_realtime
         self.suppress_tokens = suppress_tokens
         self.print_transcription_time = print_transcription_time
         self.early_transcription_on_silence = early_transcription_on_silence
@@ -376,6 +387,43 @@ def start_server(self):
             args += ['--model', self.model]
         if self.realtime_model_type:
             args += ['--realtime_model_type', self.realtime_model_type]
+        if self.download_root:
+            args += ['--root', self.download_root]
+        if self.batch_size is not None:
+            args += ['--batch', str(self.batch_size)]
+        if self.realtime_batch_size is not None:
+            args += ['--realtime_batch_size', str(self.realtime_batch_size)]
+        if self.init_realtime_after_seconds is not None:
+            args += ['--init_realtime_after_seconds', str(self.init_realtime_after_seconds)]
+        if self.initial_prompt_realtime:
+            sanitized_prompt = self.initial_prompt_realtime.replace("\n", "\\n")
+            args += ['--initial_prompt_realtime', sanitized_prompt]
+
+        # if self.compute_type:
+        #     args += ['--compute_type', self.compute_type]
+        # if self.input_device_index is not None:
+        #     args += ['--input_device_index', str(self.input_device_index)]
+        # if self.gpu_device_index is not None:
+        #     args += ['--gpu_device_index', str(self.gpu_device_index)]
+        # if self.device:
+        #     args += ['--device', self.device]
+        # if self.spinner:
+        #     args.append('--spinner')  # flag, no need for True/False
+        # if self.enable_realtime_transcription:
+        #     args.append('--enable_realtime_transcription')  # flag, no need for True/False
+        # if self.handle_buffer_overflow:
+        #     args.append('--handle_buffer_overflow')  # flag, no need for True/False
+        # if self.suppress_tokens:
+        #     args += ['--suppress_tokens', str(self.suppress_tokens)]
+        # if self.print_transcription_time:
+        #     args.append('--print_transcription_time')  # flag, no need for True/False
+        # if self.allowed_latency_limit is not None:
+        #     args += ['--allowed_latency_limit', str(self.allowed_latency_limit)]
+        # if self.no_log_file:
+        #     args.append('--no_log_file')  # flag, no need for True
+        # if self.debug_mode:
+        #     args.append('--debug')  # flag, no need for True/False
+            
         if self.language:
             args += ['--language', self.language]
         if self.silero_sensitivity is not None:

RealtimeSTT_server/stt_server.py

@@ -27,6 +27,8 @@
     - `-D, --debug`: Enable debug logging.
     - `-W, --write`: Save audio to WAV file.
     - `-s, --silence_timing`: Enable dynamic silence duration for sentence detection; default True. 
+    - `-b, --batch, --batch_size`: Batch size for inference; default 16.
+    - `--root, --download_root`: Specifies the root path were the Whisper models are downloaded to.
     - `--silero_sensitivity`: Silero VAD sensitivity (0-1); default 0.05.
     - `--silero_use_onnx`: Use Silero ONNX model; default False.
     - `--webrtc_sensitivity`: WebRTC VAD sensitivity (0-3); default 3.
@@ -38,7 +40,10 @@
     - `--early_transcription_on_silence`: Start transcription after silence in seconds; default 0.2.
     - `--beam_size`: Beam size for main model; default 5.
     - `--beam_size_realtime`: Beam size for real-time model; default 3.
-    - `--initial_prompt`: Initial transcription guidance prompt.
+    - `--init_realtime_after_seconds`: Initial waiting time for realtime transcription; default 0.2.
+    - `--realtime_batch_size`: Batch size for the real-time transcription model; default 16.
+    - `--initial_prompt`: Initial main transcription guidance prompt.
+    - `--initial_prompt_realtime`: Initial realtime transcription guidance prompt.
     - `--end_of_sentence_detection_pause`: Silence duration for sentence end detection; default 0.45.
     - `--unknown_sentence_detection_pause`: Pause duration for incomplete sentence detection; default 0.7.
     - `--mid_sentence_detection_pause`: Pause for mid-sentence break; default 2.0.
@@ -52,6 +57,14 @@
     - `--use_main_model_for_realtime`: Use main model for real-time transcription.
     - `--use_extended_logging`: Enable extensive log messages.
     - `--logchunks`: Log incoming audio chunks.
+    - `--compute_type`: Type of computation to use.
+    - `--input_device_index`: Index of the audio input device.
+    - `--gpu_device_index`: Index of the GPU device.
+    - `--device`: Device to use for computation.
+    - `--handle_buffer_overflow`: Handle buffer overflow during transcription.
+    - `--suppress_tokens`: Suppress tokens during transcription.
+    - `--allowed_latency_limit`: Allowed latency limit for real-time transcription.
+
 
 ### WebSocket Interface:
 The server supports two WebSocket connections:
@@ -364,7 +377,7 @@ def parse_arguments():
     parser.add_argument('-l', '--lang', '--language', type=str, default='en',
                 help='Language code for the STT model to transcribe in a specific language. Leave this empty for auto-detection based on input audio. Default is en. List of supported language codes: https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L11-L110')
 
-    parser.add_argument('-i', '--input-device', '--input_device_index', type=int, default=1,
+    parser.add_argument('-i', '--input-device', '--input-device-index', type=int, default=1,
                     help='Index of the audio input device to use. Use this option to specify a particular microphone or audio input device based on your system. Default is 1.')
 
     parser.add_argument('-c', '--control', '--control_port', type=int, default=8011,
@@ -378,12 +391,23 @@ def parse_arguments():
 
     parser.add_argument('-D', '--debug', action='store_true', help='Enable debug logging for detailed server operations')
 
-    parser.add_argument("-W", "--write", metavar="FILE",
-                        help="Save received audio to a WAV file")
+    parser.add_argument('-W', '--write', metavar='FILE', help='Save received audio to a WAV file')
+    
+    parser.add_argument('-b', '--batch', '--batch_size', type=int, default=16, help='Batch size for inference. This parameter controls the number of audio chunks processed in parallel during transcription. Default is 16.')
+
+    parser.add_argument('--root', '--download_root', type=str,default=None, help='Specifies the root path where the Whisper models are downloaded to. Default is None.')
 
     parser.add_argument('-s', '--silence_timing', action='store_true', default=True,
                     help='Enable dynamic adjustment of silence duration for sentence detection. Adjusts post-speech silence duration based on detected sentence structure and punctuation. Default is False.')
 
+    parser.add_argument('--init_realtime_after_seconds', type=float, default=0.2,
+                        help='The initial waiting time in seconds before real-time transcription starts. This delay helps prevent false positives at the beginning of a session. Default is 0.2 seconds.')  
+    
+    parser.add_argument('--realtime_batch_size', type=int, default=16,
+                        help='Batch size for the real-time transcription model. This parameter controls the number of audio chunks processed in parallel during real-time transcription. Default is 16.')
+    
+    parser.add_argument('--initial_prompt_realtime', type=str, default="", help='Initial prompt that guides the real-time transcription model to produce transcriptions in a particular style or format.')
+
     parser.add_argument('--silero_sensitivity', type=float, default=0.05,
                         help='Sensitivity level for Silero Voice Activity Detection (VAD), with a range from 0 to 1. Lower values make the model less sensitive, useful for noisy environments. Default is 0.05.')
 
@@ -457,6 +481,23 @@ def parse_arguments():
     parser.add_argument('--use_extended_logging', action='store_true',
                         help='Writes extensive log messages for the recording worker, that processes the audio chunks.')
 
+    parser.add_argument('--compute_type', type=str, default='default',
+                        help='Type of computation to use. See https://opennmt.net/CTranslate2/quantization.html')
+
+    parser.add_argument('--gpu_device_index', type=int, default=0,
+                        help='Index of the GPU device to use. Default is None.')
+    
+    parser.add_argument('--device', type=str, default='cuda',
+                        help='Device for model to use. Can either be "cuda" or "cpu". Default is cuda.')
+    
+    parser.add_argument('--handle_buffer_overflow', action='store_true',
+                        help='Handle buffer overflow during transcription. Default is False.')
+
+    parser.add_argument('--suppress_tokens', type=int, default=[-1], nargs='*', help='Suppress tokens during transcription. Default is [-1].')
+
+    parser.add_argument('--allowed_latency_limit', type=int, default=100,
+                        help='Maximal amount of chunks that can be unprocessed in queue before discarding chunks.. Default is 100.')
+
     parser.add_argument('--logchunks', action='store_true', help='Enable logging of incoming audio chunks (periods)')
 
     # Parse arguments
@@ -479,6 +520,9 @@ def parse_arguments():
     if args.initial_prompt:
         args.initial_prompt = args.initial_prompt.replace("\\n", "\n")
 
+    if args.initial_prompt_realtime:
+        args.initial_prompt_realtime = args.initial_prompt_realtime.replace("\\n", "\n")
+
     return args
 
 def _recorder_thread(loop):
@@ -534,7 +578,7 @@ def decode_and_resample(
 
     return resampled_audio.astype(np.int16).tobytes()
 
-async def control_handler(websocket, path):
+async def control_handler(websocket):
     debug_print(f"New control connection from {websocket.remote_address}")
     print(f"{bcolors.OKGREEN}Control client connected{bcolors.ENDC}")
     global recorder
@@ -629,7 +673,7 @@ async def control_handler(websocket, path):
     finally:
         control_connections.remove(websocket)
 
-async def data_handler(websocket, path):
+async def data_handler(websocket):
     global writechunks, wav_file
     print(f"{bcolors.OKGREEN}Data client connected{bcolors.ENDC}")
     data_connections.add(websocket)
@@ -700,8 +744,13 @@ async def main_async():
 
     recorder_config = {
         'model': args.model,
+        'download_root': args.root,
         'realtime_model_type': args.rt_model,
         'language': args.lang,
+        'batch_size': args.batch,
+        'init_realtime_after_seconds': args.init_realtime_after_seconds,
+        'realtime_batch_size': args.realtime_batch_size,
+        'initial_prompt_realtime': args.initial_prompt_realtime,
         'input_device_index': args.input_device,
         'silero_sensitivity': args.silero_sensitivity,
         'silero_use_onnx': args.silero_use_onnx,
@@ -740,6 +789,12 @@ async def main_async():
         'no_log_file': True,  # Disable logging to file
         'use_extended_logging': args.use_extended_logging,
         'level': loglevel,
+        'compute_type': args.compute_type,
+        'gpu_device_index': args.gpu_device_index,
+        'device': args.device,
+        'handle_buffer_overflow': args.handle_buffer_overflow,
+        'suppress_tokens': args.suppress_tokens,
+        'allowed_latency_limit': args.allowed_latency_limit,
     }
 
     try:

setup.py

@@ -9,7 +9,7 @@
 
 setuptools.setup(
     name="RealtimeSTT",
-    version="0.3.92",
+    version="0.3.93",
     author="Kolja Beigel",
     author_email="kolja.beigel@web.de",
     description="A fast Voice Activity Detection and Transcription System",

tests/realtimestt_speechendpoint_binary_classified.py

@@ -50,6 +50,8 @@
 
     tokenizer = DistilBertTokenizerFast.from_pretrained(model_dir)
     classification_model = DistilBertForSequenceClassification.from_pretrained(model_dir)
+    # tokenizer = DistilBertTokenizerFast.from_pretrained(model_dir, force_download=True)
+    # classification_model = DistilBertForSequenceClassification.from_pretrained(model_dir, force_download=True)
     classification_model.to(device)
     classification_model.eval()
 
@@ -85,7 +87,7 @@ def get_completion_probability(sentence, model, tokenizer, device, max_length):
     anchor_points = [
         (0.0, 1.0),
         (1.0, 0)
-    ]    
+    ]
     # anchor_points = [
     #     (0.0, 0.4),
     #     (0.5, 0.3),
@@ -144,10 +146,9 @@ def is_speech_finished(text):
     text_time_deque = deque()
 
     # Default values
-    #rapid_sentence_end_detection = 0.2
     end_of_sentence_detection_pause = 0.3
     unknown_sentence_detection_pause = 0.8
-    mid_sentence_detection_pause = 2.0
+    mid_sentence_detection_pause = 1.7
     hard_break_even_on_background_noise = 3.0
     hard_break_even_on_background_noise_min_texts = 3
     hard_break_even_on_background_noise_min_chars = 15
@@ -172,26 +173,45 @@ def text_detected(text):
     def additional_pause_based_on_words(text):
         word_count = len(text.split())
         pauses = {
-            1: 0.6,
-            2: 0.5,
-            3: 0.4,
-            4: 0.3,
-            5: 0.2,
-            6: 0.1,
+            0: 0.35,
+            1: 0.3,
+            2: 0.25,
+            3: 0.2,
+            4: 0.15,
+            5: 0.1,
+            6: 0.05,
         }
         return pauses.get(word_count, 0.0)
 
     def process_queue():
         global recorder, full_sentences, prev_text, displayed_text, rich_text_stored, text_time_deque, abrupt_stop, rapid_sentence_end_detection
 
         while True:
+            text = None  # Initialize text to ensure it's defined
+
             try:
+                # Attempt to retrieve the first item, blocking with timeout
                 text = text_queue.get(timeout=1)
             except queue.Empty:
-                continue
+                continue  # No item retrieved, continue the loop
+
+            if text is None:
+                # Exit signal received
+                break
+
+            # Drain the queue to get the latest text
+            try:
+                while True:
+                    latest_text = text_queue.get_nowait()
+                    if latest_text is None:
+                        text = None
+                        break
+                    text = latest_text
+            except queue.Empty:
+                pass  # No more items to retrieve
 
             if text is None:
-                # Exit
+                # Exit signal received after draining
                 break
 
             text = preprocess_text(text)
@@ -274,7 +294,7 @@ def process_queue():
 
     def process_text(text):
         global recorder, full_sentences, prev_text, abrupt_stop
-        #if IS_DEBUG: print(f"SENTENCE: post_speech_silence_duration: {recorder.post_speech_silence_duration}")
+        if IS_DEBUG: print(f"SENTENCE: post_speech_silence_duration: {recorder.post_speech_silence_duration}")
         recorder.post_speech_silence_duration = unknown_sentence_detection_pause
         text = preprocess_text(text)
         text = text.rstrip()
@@ -312,7 +332,7 @@ def process_text(text):
         'beam_size': 5,
         'beam_size_realtime': 3,
         'no_log_file': True,
-        'initial_prompt': (
+        'initial_prompt_realtime': (
             "End incomplete sentences with ellipses.\n"
             "Examples:\n"
             "Complete: The sky is blue.\n"