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remove duplicate code

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This commit is contained in:
makaveli10
2024-01-10 14:14:48 +00:00
parent 0f9e93d203
commit 244ca9e6ba
3 changed files with 366 additions and 548 deletions
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whisper_live/server.py
+364 -95
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@@ -5,14 +5,24 @@ import json
import textwrap
import logging
# logging.basicConfig(level = logging.INFO)
logging.basicConfig(level = logging.INFO)
from websockets.sync.server import serve
import torch
import numpy as np
import time
import queue
from whisper_live.vad import VoiceActivityDetection
from scipy.io.wavfile import write
import functools
from whisper_live.vad import VoiceActivityDetection
from whisper_live.transcriber import WhisperModel
try:
from whisper_live.transcriber_tensorrt import WhisperTRTLLM
except Exception as e:
logging.warn("cannot import WhisperTRTLLM")
class TranscriptionServer:
@@ -34,7 +44,7 @@ class TranscriptionServer:
def __init__(self):
# voice activity detection model
self.clients = {}
self.websockets = {}
self.clients_start_time = {}
@@ -58,7 +68,7 @@ class TranscriptionServer:
return wait_time / 60
def recv_audio(self, websocket):
def recv_audio(self, websocket, backend="tensorrt", whisper_tensorrt_path=None):
"""
Receive audio chunks from a client in an infinite loop.
@@ -79,6 +89,11 @@ class TranscriptionServer:
Raises:
Exception: If there is an error during the audio frame processing.
"""
self.backend = backend
if self.backend == "tensorrt":
self.vad_model = VoiceActivityDetection()
self.vad_threshold = 0.5
logging.info("New client connected")
options = websocket.recv()
options = json.loads(options)
@@ -96,25 +111,72 @@ class TranscriptionServer:
del websocket
return
client = ServeClient(
websocket,
multilingual=options["multilingual"],
language=options["language"],
task=options["task"],
client_uid=options["uid"],
model_size=options["model_size"],
initial_prompt=options["initial_prompt"],
vad_parameters=options["vad_parameters"]
)
if self.backend == "tensorrt":
try:
import tensorrt as trt
import tensorrt_llm
self.backend = "tensorrt"
client = ServeClientTensorRT(
websocket,
multilingual=options["multilingual"],
language=options["language"],
task=options["task"],
client_uid=options["uid"],
model_path=whisper_tensorrt_path
)
logging.info(f"Running TensorRT backend.")
except Exception as e:
websocket.send(
json.dumps(
{
"uid": self.client_uid,
"status": "ERROR",
"message": f"TensorRT-LLM not supported on Server yet. Reverting to available backend: 'faster_whisper'"
}
)
)
self.backend = "faster_whisper"
if self.backend == "faster_whisper":
client = ServeClientFasterWhisper(
websocket,
multilingual=options["multilingual"],
language=options["language"],
task=options["task"],
client_uid=options["uid"],
model_size=options["model_size"],
initial_prompt=options.get("initial_prompt"),
vad_parameters=options.get("vad_parameters")
)
logging.info(f"Running faster_whisper backend.")
self.clients[websocket] = client
self.clients_start_time[websocket] = time.time()
no_voice_activity_chunks = 0
while True:
try:
frame_data = websocket.recv()
frame_np = np.frombuffer(frame_data, dtype=np.float32)
# VAD
if self.backend == "tensorrt":
try:
speech_prob = self.vad_model(torch.from_numpy(frame_np.copy()), self.RATE).item()
if speech_prob < self.vad_threshold:
no_voice_activity_chunks += 1
if no_voice_activity_chunks > 3:
if not self.clients[websocket].eos:
self.clients[websocket].set_eos(True)
time.sleep(0.1) # EOS stop receiving frames for a 100ms(to send output to LLM.)
continue
no_voice_activity_chunks = 0
self.clients[websocket].set_eos(False)
except Exception as e:
logging.error(e)
return
self.clients[websocket].add_frames(frame_np)
elapsed_time = time.time() - self.clients_start_time[websocket]
@@ -130,8 +192,7 @@ class TranscriptionServer:
except Exception as e:
logging.error(e)
if self.clients[websocket].model_size is not None:
self.clients[websocket].cleanup()
self.clients[websocket].cleanup()
self.clients.pop(websocket)
self.clients_start_time.pop(websocket)
logging.info("Connection Closed.")
@@ -139,7 +200,7 @@ class TranscriptionServer:
del websocket
break
def run(self, host, port=9090):
def run(self, host, port=9090, backend="tensorrt", whisper_tensorrt_path=None):
"""
Run the transcription server.
@@ -147,11 +208,294 @@ class TranscriptionServer:
host (str): The host address to bind the server.
port (int): The port number to bind the server.
"""
with serve(self.recv_audio, host, port) as server:
with serve(
functools.partial(
self.recv_audio,
backend=backend,
whisper_tensorrt_path=whisper_tensorrt_path
),
host,
port
) as server:
server.serve_forever()
class ServeClient:
class ServeClientBase(object):
def __init__(self, client_uid, websocket):
self.client_uid = client_uid
self.websocket = websocket
self.data = b""
self.frames = b""
self.timestamp_offset = 0.0
self.frames_np = None
self.frames_offset = 0.0
self.text = []
self.current_out = ''
self.prev_out = ''
self.t_start=None
self.exit = False
self.same_output_threshold = 0
self.show_prev_out_thresh = 5 # if pause(no output from whisper) show previous output for 5 seconds
self.add_pause_thresh = 3 # add a blank to segment list as a pause(no speech) for 3 seconds
self.transcript = []
self.send_last_n_segments = 10
# text formatting
self.wrapper = textwrap.TextWrapper(width=50)
self.pick_previous_segments = 2
# threading
self.lock = threading.Lock()
def add_frames(self, frame_np):
"""
Add audio frames to the ongoing audio stream buffer.
This method is responsible for maintaining the audio stream buffer, allowing the continuous addition
of audio frames as they are received. It also ensures that the buffer does not exceed a specified size
to prevent excessive memory usage.
If the buffer size exceeds a threshold (45 seconds of audio data), it discards the oldest 30 seconds
of audio data to maintain a reasonable buffer size. If the buffer is empty, it initializes it with the provided
audio frame. The audio stream buffer is used for real-time processing of audio data for transcription.
Args:
frame_np (numpy.ndarray): The audio frame data as a NumPy array.
"""
self.lock.acquire()
if self.frames_np is not None and self.frames_np.shape[0] > 45*self.RATE:
self.frames_offset += 30.0
self.frames_np = self.frames_np[int(30*self.RATE):]
if self.frames_np is None:
self.frames_np = frame_np.copy()
else:
self.frames_np = np.concatenate((self.frames_np, frame_np), axis=0)
self.lock.release()
def speech_to_text(self):
raise NotImplementedError("Please implement in child Class.")
def disconnect(self):
"""
Notify the client of disconnection and send a disconnect message.
This method sends a disconnect message to the client via the WebSocket connection to notify them
that the transcription service is disconnecting gracefully.
"""
self.websocket.send(
json.dumps(
{
"uid": self.client_uid,
"message": self.DISCONNECT
}
)
)
def cleanup(self):
"""
Perform cleanup tasks before exiting the transcription service.
This method performs necessary cleanup tasks, including stopping the transcription thread, marking
the exit flag to indicate the transcription thread should exit gracefully, and destroying resources
associated with the transcription process.
"""
logging.info("Cleaning up.")
self.exit = True
self.transcriber.destroy()
class ServeClientTensorRT(ServeClientBase):
"""
Attributes:
RATE (int): The audio sampling rate (constant) set to 16000.
SERVER_READY (str): A constant message indicating that the server is ready.
DISCONNECT (str): A constant message indicating that the client should disconnect.
client_uid (str): A unique identifier for the client.
data (bytes): Accumulated audio data.
frames (bytes): Accumulated audio frames.
language (str): The language for transcription.
task (str): The task type, e.g., "transcribe."
transcriber (WhisperModel): The Whisper model for speech-to-text.
timestamp_offset (float): The offset in audio timestamps.
frames_np (numpy.ndarray): NumPy array to store audio frames.
frames_offset (float): The offset in audio frames.
text (list): List of transcribed text segments.
current_out (str): The current incomplete transcription.
prev_out (str): The previous incomplete transcription.
t_start (float): Timestamp for the start of transcription.
exit (bool): A flag to exit the transcription thread.
same_output_threshold (int): Threshold for consecutive same output segments.
show_prev_out_thresh (int): Threshold for showing previous output segments.
add_pause_thresh (int): Threshold for adding a pause (blank) segment.
transcript (list): List of transcribed segments.
send_last_n_segments (int): Number of last segments to send to the client.
wrapper (textwrap.TextWrapper): Text wrapper for formatting text.
pick_previous_segments (int): Number of previous segments to include in the output.
websocket: The WebSocket connection for the client.
"""
RATE = 16000
SERVER_READY = "SERVER_READY"
DISCONNECT = "DISCONNECT"
def __init__(
self,
websocket,
task="transcribe",
device=None,
multilingual=False,
language=None,
client_uid=None,
model_path=None
):
"""
Initialize a ServeClient instance.
The Whisper model is initialized based on the client's language and device availability.
The transcription thread is started upon initialization. A "SERVER_READY" message is sent
to the client to indicate that the server is ready.
Args:
websocket (WebSocket): The WebSocket connection for the client.
task (str, optional): The task type, e.g., "transcribe." Defaults to "transcribe".
device (str, optional): The device type for Whisper, "cuda" or "cpu". Defaults to None.
multilingual (bool, optional): Whether the client supports multilingual transcription. Defaults to False.
language (str, optional): The language for transcription. Defaults to None.
client_uid (str, optional): A unique identifier for the client. Defaults to None.
"""
super().__init__(client_uid, websocket)
self.language = language if multilingual else "en"
self.task = task
self.eos = False
self.transcriber = WhisperTRTLLM(model_path, False, "assets", device="cuda")
# threading
self.trans_thread = threading.Thread(target=self.speech_to_text)
self.trans_thread.start()
self.websocket.send(
json.dumps(
{
"uid": self.client_uid,
"message": self.SERVER_READY
}
)
)
def set_eos(self, eos):
self.lock.acquire()
self.eos = eos
self.lock.release()
def add_frames(self, frame_np):
"""
Add audio frames to the ongoing audio stream buffer.
This method is responsible for maintaining the audio stream buffer, allowing the continuous addition
of audio frames as they are received. It also ensures that the buffer does not exceed a specified size
to prevent excessive memory usage.
If the buffer size exceeds a threshold (45 seconds of audio data), it discards the oldest 30 seconds
of audio data to maintain a reasonable buffer size. If the buffer is empty, it initializes it with the provided
audio frame. The audio stream buffer is used for real-time processing of audio data for transcription.
Args:
frame_np (numpy.ndarray): The audio frame data as a NumPy array.
"""
self.lock.acquire()
if self.frames_np is not None and self.frames_np.shape[0] > 45*self.RATE:
self.frames_offset += 30.0
self.frames_np = self.frames_np[int(30*self.RATE):]
if self.frames_np is None:
self.frames_np = frame_np.copy()
else:
self.frames_np = np.concatenate((self.frames_np, frame_np), axis=0)
self.lock.release()
def speech_to_text(self):
"""
Process an audio stream in an infinite loop, continuously transcribing the speech.
This method continuously receives audio frames, performs real-time transcription, and sends
transcribed segments to the client via a WebSocket connection.
If the client's language is not detected, it waits for 30 seconds of audio input to make a language prediction.
It utilizes the Whisper ASR model to transcribe the audio, continuously processing and streaming results. Segments
are sent to the client in real-time, and a history of segments is maintained to provide context.Pauses in speech
(no output from Whisper) are handled by showing the previous output for a set duration. A blank segment is added if
there is no speech for a specified duration to indicate a pause.
Raises:
Exception: If there is an issue with audio processing or WebSocket communication.
"""
while True:
if self.exit:
logging.info("Exiting speech to text thread")
break
if self.frames_np is None:
time.sleep(0.02) # wait for any audio to arrive
continue
# clip audio if the current chunk exceeds 30 seconds, this basically implies that
# no valid segment for the last 30 seconds from whisper
if self.frames_np[int((self.timestamp_offset - self.frames_offset)*self.RATE):].shape[0] > 25 * self.RATE:
duration = self.frames_np.shape[0] / self.RATE
self.timestamp_offset = self.frames_offset + duration - 5
samples_take = max(0, (self.timestamp_offset - self.frames_offset)*self.RATE)
input_bytes = self.frames_np[int(samples_take):].copy()
duration = input_bytes.shape[0] / self.RATE
if duration<0.4:
continue
try:
input_sample = input_bytes.copy()
mel, duration = self.transcriber.log_mel_spectrogram(input_sample)
last_segment = self.transcriber.transcribe(mel)
segments = []
if len(last_segment):
if len(self.transcript) < self.send_last_n_segments:
segments = self.transcript[:].copy()
else:
segments = self.transcript[-self.send_last_n_segments:].copy()
print(self.transcript, len(self.transcript))
if last_segment is not None:
segments.append({"text": last_segment})
try:
self.websocket.send(
json.dumps({
"uid": self.client_uid,
"segments": segments,
})
)
if self.eos:
print("EOS is true: ", self.timestamp_offset, duration)
if not len(self.transcript):
self.transcript.append({"text": last_segment + " "})
elif self.transcript[-1]["text"].strip() != last_segment:
self.transcript.append({"text": last_segment + " "})
self.timestamp_offset += duration
# self.set_eos(False)
# logging.info(
# f"[INFO:] Processed : {self.timestamp_offset} seconds / {self.frames_np.shape[0] / self.RATE} seconds"
# )
except Exception as e:
logging.error(f"[ERROR]: {e}")
except Exception as e:
logging.error(f"[ERROR]: {e}")
class ServeClientFasterWhisper(ServeClientBase):
"""
Attributes:
RATE (int): The audio sampling rate (constant) set to 16000.
@@ -211,9 +555,7 @@ class ServeClient:
client_uid (str, optional): A unique identifier for the client. Defaults to None.
"""
self.client_uid = client_uid
self.data = b""
self.frames = b""
super().__init__(client_uid, websocket)
self.model_sizes = [
"tiny", "base", "small", "medium", "large-v2", "large-v3"
]
@@ -221,7 +563,6 @@ class ServeClient:
self.model_size = self.get_model_size(model_size)
self.language = language if self.multilingual else "en"
self.task = task
self.websocket = websocket
self.initial_prompt = initial_prompt
self.vad_parameters = vad_parameters or {"threshold": 0.5}
@@ -236,24 +577,6 @@ class ServeClient:
compute_type="int8" if device=="cpu" else "float16",
local_files_only=False,
)
self.timestamp_offset = 0.0
self.frames_np = None
self.frames_offset = 0.0
self.text = []
self.current_out = ''
self.prev_out = ''
self.t_start=None
self.exit = False
self.same_output_threshold = 0
self.show_prev_out_thresh = 5 # if pause(no output from whisper) show previous output for 5 seconds
self.add_pause_thresh = 3 # add a blank to segment list as a pause(no speech) for 3 seconds
self.transcript = []
self.send_last_n_segments = 10
# text formatting
self.wrapper = textwrap.TextWrapper(width=50)
self.pick_previous_segments = 2
# threading
self.trans_thread = threading.Thread(target=self.speech_to_text)
@@ -292,30 +615,6 @@ class ServeClient:
return model_size
def add_frames(self, frame_np):
"""
Add audio frames to the ongoing audio stream buffer.
This method is responsible for maintaining the audio stream buffer, allowing the continuous addition
of audio frames as they are received. It also ensures that the buffer does not exceed a specified size
to prevent excessive memory usage.
If the buffer size exceeds a threshold (45 seconds of audio data), it discards the oldest 30 seconds
of audio data to maintain a reasonable buffer size. If the buffer is empty, it initializes it with the provided
audio frame. The audio stream buffer is used for real-time processing of audio data for transcription.
Args:
frame_np (numpy.ndarray): The audio frame data as a NumPy array.
"""
if self.frames_np is not None and self.frames_np.shape[0] > 45*self.RATE:
self.frames_offset += 30.0
self.frames_np = self.frames_np[int(30*self.RATE):]
if self.frames_np is None:
self.frames_np = frame_np.copy()
else:
self.frames_np = np.concatenate((self.frames_np, frame_np), axis=0)
def speech_to_text(self):
"""
Process an audio stream in an infinite loop, continuously transcribing the speech.
@@ -489,33 +788,3 @@ class ServeClient:
self.timestamp_offset += offset
return last_segment
def disconnect(self):
"""
Notify the client of disconnection and send a disconnect message.
This method sends a disconnect message to the client via the WebSocket connection to notify them
that the transcription service is disconnecting gracefully.
"""
self.websocket.send(
json.dumps(
{
"uid": self.client_uid,
"message": self.DISCONNECT
}
)
)
def cleanup(self):
"""
Perform cleanup tasks before exiting the transcription service.
This method performs necessary cleanup tasks, including stopping the transcription thread, marking
the exit flag to indicate the transcription thread should exit gracefully, and destroying resources
associated with the transcription process.
"""
logging.info("Cleaning up.")
self.exit = True
self.transcriber.destroy()