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🔨 refactor whisper_live according to flake8

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This commit is contained in:
makaveli10
2024-02-09 13:45:13 +05:30
parent b4abe95fc6
commit 9fbff47126
13 changed files with 549 additions and 540 deletions
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whisper_live/__version__.py
+1 -1
View File
@@ -1 +1 @@
__version__="0.1.0"
__version__ = "0.1.0"
whisper_live/client.py
+57 -124
View File
@@ -2,68 +2,14 @@ import os
import wave
import numpy as np
import scipy
import ffmpeg
import pyaudio
import threading
import textwrap
import json
import websocket
import uuid
import time
def format_time(s):
"""Convert seconds (float) to SRT time format."""
hours = int(s // 3600)
minutes = int((s % 3600) // 60)
seconds = int(s % 60)
milliseconds = int((s - int(s)) * 1000)
return f"{hours:02}:{minutes:02}:{seconds:02},{milliseconds:03}"
def create_srt_file(segments, output_file):
with open(output_file, 'w', encoding='utf-8') as srt_file:
segment_number = 1
for segment in segments:
start_time = format_time(float(segment['start']))
end_time = format_time(float(segment['end']))
text = segment['text']
srt_file.write(f"{segment_number}\n")
srt_file.write(f"{start_time} --> {end_time}\n")
srt_file.write(f"{text}\n\n")
segment_number += 1
def resample(file: str, sr: int = 16000):
"""
# https://github.com/openai/whisper/blob/7858aa9c08d98f75575035ecd6481f462d66ca27/whisper/audio.py#L22
Open an audio file and read as mono waveform, resampling as necessary,
save the resampled audio
Args:
file (str): The audio file to open
sr (int): The sample rate to resample the audio if necessary
Returns:
resampled_file (str): The resampled audio file
"""
try:
# This launches a subprocess to decode audio while down-mixing and resampling as necessary.
# Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
out, _ = (
ffmpeg.input(file, threads=0)
.output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
)
except ffmpeg.Error as e:
raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
np_buffer = np.frombuffer(out, dtype=np.int16)
resampled_file = f"{file.split('.')[0]}_resampled.wav"
scipy.io.wavfile.write(resampled_file, sr, np_buffer.astype(np.int16))
return resampled_file
import ffmpeg
import whisper_live.utils as utils
class Client:
@@ -150,10 +96,40 @@ class Client:
self.transcript = []
print("[INFO]: * recording")
def handle_status_messages(self, message_data):
"""Handles server status messages."""
status = message_data["status"]
if status == "WAIT":
self.waiting = True
print(f"[INFO]: Server is full. Estimated wait time {round(message_data['message'])} minutes.")
elif status == "ERROR":
print(f"Message from Server: {message_data['message']}")
self.server_error = True
elif status == "WARNING":
print(f"Message from Server: {message_data['message']}")
def process_segments(self, segments):
"""Processes transcript segments."""
text = []
for i, seg in enumerate(segments):
if not text or text[-1] != seg["text"]:
text.append(seg["text"])
if i == len(segments) - 1:
self.last_segment = seg
elif (self.server_backend == "faster_whisper" and
(not self.transcript or
float(seg['start']) >= float(self.transcript[-1]['end']))):
self.transcript.append(seg)
# Truncate to last 3 entries for brevity.
text = text[-3:]
# utils.clear_screen()
utils.print_transcript(text)
def on_message(self, ws, message):
"""
Callback function called when a message is received from the server.
It updates various attributes of the client based on the received message, including
recording status, language detection, and server messages. If a disconnect message
is received, it sets the recording status to False.
@@ -171,14 +147,7 @@ class Client:
return
if "status" in message.keys():
if message["status"] == "WAIT":
self.waiting = True
print(
f"[INFO]:Server is full. Estimated wait time {round(message['message'])} minutes."
)
elif message["status"] == "ERROR":
print(f"Message from Server: {message['message']}")
self.server_error = True
self.handle_status_messages(message)
return
if "message" in message.keys() and message["message"] == "DISCONNECT":
@@ -199,38 +168,8 @@ class Client:
)
return
if "segments" not in message.keys():
return
message = message["segments"]
text = []
n_segments = len(message)
if n_segments:
for i, seg in enumerate(message):
if text and text[-1] == seg["text"]:
# already got it
continue
text.append(seg["text"])
if i == n_segments-1:
self.last_segment = seg
elif self.server_backend == "faster_whisper":
if not len(self.transcript) or float(seg['start']) >= float(self.transcript[-1]['end']):
self.transcript.append(seg)
# keep only last 3
if len(text) > 3:
text = text[-3:]
wrapper = textwrap.TextWrapper(width=60)
word_list = wrapper.wrap(text="".join(text))
# Print each line.
if os.name == "nt":
os.system("cls")
else:
os.system("clear")
for element in word_list:
print(element)
if "segments" in message.keys():
self.process_segments(message["segments"])
def on_error(self, ws, error):
print(f"[ERROR] WebSocket Error: {error}")
@@ -246,7 +185,7 @@ class Client:
def on_open(self, ws):
"""
Callback function called when the WebSocket connection is successfully opened.
Sends an initial configuration message to the server, including client UID,
language selection, and task type.
@@ -270,8 +209,8 @@ class Client:
def bytes_to_float_array(audio_bytes):
"""
Convert audio data from bytes to a NumPy float array.
It assumes that the audio data is in 16-bit PCM format. The audio data is normalized to
It assumes that the audio data is in 16-bit PCM format. The audio data is normalized to
have values between -1 and 1.
Args:
@@ -299,10 +238,10 @@ class Client:
def play_file(self, filename):
"""
Play an audio file and send it to the server for processing.
Reads an audio file, plays it through the audio output, and simultaneously sends
the audio data to the server for processing. It uses PyAudio to create an audio
stream for playback. The audio data is read from the file in chunks, converted to
the audio data to the server for processing. It uses PyAudio to create an audio
stream for playback. The audio data is read from the file in chunks, converted to
floating-point format, and sent to the server using WebSocket communication.
This method is typically used when you want to process pre-recorded audio and send it
to the server in real-time.
@@ -310,7 +249,7 @@ class Client:
Args:
filename (str): The path to the audio file to be played and sent to the server.
"""
# read audio and create pyaudio stream
with wave.open(filename, "rb") as wavfile:
self.stream = self.p.open(
@@ -356,7 +295,7 @@ class Client:
"""
Close the WebSocket connection and join the WebSocket thread.
First attempts to close the WebSocket connection using `self.client_socket.close()`. After
First attempts to close the WebSocket connection using `self.client_socket.close()`. After
closing the connection, it joins the WebSocket thread to ensure proper termination.
"""
@@ -383,7 +322,7 @@ class Client:
"""
Write audio frames to a WAV file.
The WAV file is created or overwritten with the specified name. The audio frames should be
The WAV file is created or overwritten with the specified name. The audio frames should be
in the correct format and match the specified channel, sample width, and sample rate.
Args:
@@ -433,7 +372,6 @@ class Client:
print("[INFO]: HLS stream processing finished.")
def record(self, out_file="output_recording.wav"):
"""
Record audio data from the input stream and save it to a WAV file.
@@ -444,11 +382,12 @@ class Client:
Audio data is saved in chunks to the "chunks" directory. Each chunk is saved as a separate WAV file.
The recording will continue until the specified duration is reached or until the `RECORDING` flag is set to `False`.
The recording process can be interrupted by sending a KeyboardInterrupt (e.g., pressing Ctrl+C). After recording,
The recording process can be interrupted by sending a KeyboardInterrupt (e.g., pressing Ctrl+C). After recording,
the method combines all the saved audio chunks into the specified `out_file`.
Args:
out_file (str, optional): The name of the output WAV file to save the entire recording. Default is "output_recording.wav".
out_file (str, optional): The name of the output WAV file to save the entire recording.
Default is "output_recording.wav".
"""
n_audio_file = 0
@@ -458,7 +397,7 @@ class Client:
for _ in range(0, int(self.rate / self.chunk * self.record_seconds)):
if not self.recording:
break
data = self.stream.read(self.chunk, exception_on_overflow = False)
data = self.stream.read(self.chunk, exception_on_overflow=False)
self.frames += data
audio_array = Client.bytes_to_float_array(data)
@@ -498,8 +437,8 @@ class Client:
def write_output_recording(self, n_audio_file, out_file):
"""
Combine and save recorded audio chunks into a single WAV file.
The individual audio chunk files are expected to be located in the "chunks" directory. Reads each chunk
The individual audio chunk files are expected to be located in the "chunks" directory. Reads each chunk
file, appends its audio data to the final recording, and then deletes the chunk file. After combining
and saving, the final recording is stored in the specified `out_file`.
@@ -532,7 +471,7 @@ class Client:
def write_srt_file(self, output_path="output.srt"):
self.transcript.append(self.last_segment)
create_srt_file(self.transcript, output_path)
utils.create_srt_file(self.transcript, output_path)
class TranscriptionClient:
@@ -558,13 +497,7 @@ class TranscriptionClient:
transcription_client()
```
"""
def __init__(self,
host,
port,
lang=None,
translate=False,
model="small",
):
def __init__(self, host, port, lang=None, translate=False, model="small"):
self.client = Client(host, port, lang, translate, model)
def __call__(self, audio=None, hls_url=None):
@@ -572,12 +505,12 @@ class TranscriptionClient:
Start the transcription process.
Initiates the transcription process by connecting to the server via a WebSocket. It waits for the server
to be ready to receive audio data and then sends audio for transcription. If an audio file is provided, it
to be ready to receive audio data and then sends audio for transcription. If an audio file is provided, it
will be played and streamed to the server; otherwise, it will perform live recording.
Args:
audio (str, optional): Path to an audio file for transcription. Default is None, which triggers live recording.
"""
print("[INFO]: Waiting for server ready ...")
while not self.client.recording:
@@ -589,7 +522,7 @@ class TranscriptionClient:
if hls_url is not None:
self.client.process_hls_stream(hls_url)
elif audio is not None:
resampled_file = resample(audio)
resampled_file = utils.resample(audio)
self.client.play_file(resampled_file)
else:
self.client.record()
self.client.record()
whisper_live/server.py
+334 -309
View File
@@ -3,25 +3,80 @@ import time
import threading
import json
import textwrap
import functools
import logging
logging.basicConfig(level = logging.INFO)
from websockets.sync.server import serve
import torch
import numpy as np
from whisper_live.vad import VoiceActivityDetection
import functools
from websockets.sync.server import serve
from whisper_live.vad import VoiceActivityDetection
from whisper_live.transcriber import WhisperModel
try:
from whisper_live.transcriber_tensorrt import WhisperTRTLLM
except Exception as e:
except Exception:
pass
logging.basicConfig(level=logging.INFO)
class VoiceActivityDetector:
def __init__(self, threshold=0.5):
self.model = VoiceActivityDetection()
self.threshold = threshold
def __call__(self, audio_frame):
speech_prob = self.model(torch.from_numpy(audio_frame), TranscriptionServer.RATE).item()
return speech_prob > self.threshold
class ClientManager:
def __init__(self, max_clients=4, max_connection_time=600):
self.clients = {}
self.start_times = {}
self.max_clients = max_clients
self.max_connection_time = max_connection_time
def add_client(self, websocket, client):
self.clients[websocket] = client
self.start_times[websocket] = time.time()
def get_client(self, websocket):
if websocket in self.clients:
return self.clients[websocket]
return False
def remove_client(self, websocket):
client = self.clients.pop(websocket, None)
if client:
client.cleanup()
self.start_times.pop(websocket, None)
def get_wait_time(self):
"""Calculate and return the estimated wait time for clients."""
wait_time = None
for start_time in self.start_times.values():
current_client_time_remaining = self.max_connection_time - (time.time() - start_time)
if wait_time is None or current_client_time_remaining < wait_time:
wait_time = current_client_time_remaining
return wait_time / 60 if wait_time is not None else 0
def is_server_full(self, websocket, options):
"""Check if the server is full and send wait message if necessary."""
if len(self.clients) >= self.max_clients:
wait_time = self.get_wait_time()
response = {"uid": options["uid"], "status": "WAIT", "message": wait_time}
websocket.send(json.dumps(response))
return True
return False
def is_client_timeout(self, websocket):
elapsed_time = time.time() - self.start_times[websocket]
if elapsed_time >= self.max_connection_time:
self.clients[websocket].disconnect()
logging.warning(f"Client with uid '{self.clients[websocket].client_uid}' disconnected due to overtime.")
return True
return False
class TranscriptionServer:
"""
@@ -42,12 +97,8 @@ class TranscriptionServer:
def __init__(self):
# voice activity detection model
self.clients = {}
self.websockets = {}
self.clients_start_time = {}
self.max_clients = 4
self.max_connection_time = 600
self.client_manager = ClientManager()
self.no_voice_activity_chunks = 0
def get_wait_time(self):
"""
@@ -58,7 +109,7 @@ class TranscriptionServer:
"""
wait_time = None
for k, v in self.clients_start_time.items():
for _, v in self.clients_start_time.items():
current_client_time_remaining = self.max_connection_time - (time.time() - v)
if wait_time is None or current_client_time_remaining < wait_time:
@@ -66,6 +117,64 @@ class TranscriptionServer:
return wait_time / 60
def is_server_full(self, websocket, options):
if len(self.clients) >= self.max_clients:
wait_time = self.get_wait_time()
response = {"uid": options["uid"], "status": "WAIT", "message": wait_time}
websocket.send(json.dumps(response))
websocket.close()
return True
return False
def initialize_client(
self, websocket, options, faster_whisper_custom_model_path,
whisper_tensorrt_path, trt_multilingual
):
if self.backend == "tensorrt":
try:
client = ServeClientTensorRT(
websocket,
multilingual=trt_multilingual,
language=options["language"],
task=options["task"],
client_uid=options["uid"],
model=whisper_tensorrt_path
)
logging.info("Running TensorRT backend.")
except Exception as e:
logging.error(f"TensorRT-LLM not supported: {e}")
self.client_uid = options["uid"]
websocket.send(json.dumps({
"uid": self.client_uid,
"status": "WARNING",
"message": "TensorRT-LLM not supported on Server yet. "
"Reverting to available backend: 'faster_whisper'"
}))
self.backend = "faster_whisper"
if self.backend == "faster_whisper":
if faster_whisper_custom_model_path is not None and os.path.exists(faster_whisper_custom_model_path):
logging.info(f"Using custom model {faster_whisper_custom_model_path}")
options["model"] = faster_whisper_custom_model_path
client = ServeClientFasterWhisper(
websocket,
language=options["language"],
task=options["task"],
client_uid=options["uid"],
model=options["model"],
initial_prompt=options.get("initial_prompt"),
vad_parameters=options.get("vad_parameters")
)
logging.info("Running faster_whisper backend.")
# self.clients[websocket] = client
# self.clients_start_time[websocket] = time.time()
self.client_manager.add_client(websocket, client)
def get_audio_from_websocket(self, websocket):
frame_data = websocket.recv()
return np.frombuffer(frame_data, dtype=np.float32)
def recv_audio(self,
websocket,
backend="faster_whisper",
@@ -74,7 +183,7 @@ class TranscriptionServer:
trt_multilingual=False):
"""
Receive audio chunks from a client in an infinite loop.
Continuously receives audio frames from a connected client
over a WebSocket connection. It processes the audio frames using a
voice activity detection (VAD) model to determine if they contain speech
@@ -96,127 +205,53 @@ 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)
if len(self.clients) >= self.max_clients:
logging.warning("Client Queue Full. Asking client to wait ...")
wait_time = self.get_wait_time()
response = {
"uid": options["uid"],
"status": "WAIT",
"message": wait_time,
}
websocket.send(json.dumps(response))
if self.client_manager.is_server_full(websocket, options):
websocket.close()
del websocket
return
self.backend = backend
if self.backend == "tensorrt":
try:
import tensorrt as trt
import tensorrt_llm
self.backend = "tensorrt"
client = ServeClientTensorRT(
websocket,
multilingual=trt_multilingual,
language=options["language"],
task=options["task"],
client_uid=options["uid"],
model=whisper_tensorrt_path
)
logging.info(f"Running TensorRT backend.")
except Exception as e:
self.client_uid = options["uid"]
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"
self.vad_detector = VoiceActivityDetector()
if self.backend == "faster_whisper":
# validate custom model
if faster_whisper_custom_model_path is not None and os.path.exists(faster_whisper_custom_model_path):
logging.info(f"Using custom model {faster_whisper_custom_model_path}")
options["model"] = faster_whisper_custom_model_path
client = ServeClientFasterWhisper(
websocket,
language=options["language"],
task=options["task"],
client_uid=options["uid"],
model=options["model"],
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
self.initialize_client(
websocket, options, faster_whisper_custom_model_path, whisper_tensorrt_path, trt_multilingual)
while True:
while not self.client_manager.is_client_timeout(websocket):
try:
frame_data = websocket.recv()
frame_np = np.frombuffer(frame_data, dtype=np.float32)
frame_np = self.get_audio_from_websocket(websocket)
client = self.client_manager.get_client(websocket)
# VAD, for faster_whisper VAD model is already integrated
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) # Sleep 100m; wait some voice activity.
continue
no_voice_activity_chunks = 0
self.clients[websocket].set_eos(False)
if not self.voice_activity(websocket, frame_np):
continue
self.no_voice_activity_chunks = 0
client.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]
if elapsed_time >= self.max_connection_time:
self.clients[websocket].disconnect()
logging.warning(f"Client with uid '{self.clients[websocket].client_uid}' disconnected due to overtime.")
self.clients[websocket].cleanup()
self.clients.pop(websocket)
self.clients_start_time.pop(websocket)
websocket.close()
del websocket
break
client.add_frames(frame_np)
except Exception as e:
logging.error(e)
self.clients[websocket].cleanup()
self.clients.pop(websocket)
self.clients_start_time.pop(websocket)
del websocket
self.cleanup(websocket)
websocket.close()
break
def run(self,
host,
port=9090,
backend="tensorrt",
if self.client_manager.get_client(websocket):
self.cleanup(websocket)
websocket.close()
del websocket
def run(self,
host,
port=9090,
backend="tensorrt",
faster_whisper_custom_model_path=None,
whisper_tensorrt_path=None,
trt_multilingual=False
):
whisper_tensorrt_path=None,
trt_multilingual=False):
"""
Run the transcription server.
@@ -237,6 +272,21 @@ class TranscriptionServer:
) as server:
server.serve_forever()
def voice_activity(self, websocket, frame_np):
if not self.vad_detector(frame_np):
self.no_voice_activity_chunks += 1
if self.no_voice_activity_chunks > 3:
client = self.client_manager.get_client(websocket)
if not client.eos:
client.set_eos(True)
time.sleep(0.1) # Sleep 100m; wait some voice activity.
return False
return True
def cleanup(self, websocket):
if self.client_manager.get_client(websocket):
self.client_manager.remove_client(websocket)
class ServeClientBase(object):
RATE = 16000
@@ -254,7 +304,7 @@ class ServeClientBase(object):
self.text = []
self.current_out = ''
self.prev_out = ''
self.t_start=None
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
@@ -268,7 +318,16 @@ class ServeClientBase(object):
# threading
self.lock = threading.Lock()
def speech_to_text(self):
raise NotImplementedError
def transcribe_audio(self):
raise NotImplementedError
def handle_transcription_output(self):
raise NotImplementedError
def add_frames(self, frame_np):
"""
Add audio frames to the ongoing audio stream buffer.
@@ -295,9 +354,50 @@ class ServeClientBase(object):
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 clip_audio_if_no_valid_segment(self):
"""
Update the timestamp offset based on audio buffer status.
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
def get_audio_chunk_for_processing(self):
"""Retrieve the next chunk of audio data for processing."""
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
return input_bytes, duration
def prepare_segments(self, last_segment=None):
"""Prepare the segments to be sent to the client."""
segments = []
if len(self.transcript) >= self.send_last_n_segments:
segments = self.transcript[-self.send_last_n_segments:].copy()
else:
segments = self.transcript.copy()
if last_segment is not None:
segments = segments + [last_segment]
return segments
def get_audio_chunk_duration(self, input_bytes):
"""Calculate the duration of the current audio chunk."""
return input_bytes.shape[0] / self.RATE
def send_transcription_to_client(self, segments):
"""Send the transcription segments to the client."""
try:
self.websocket.send(
json.dumps({
"uid": self.client_uid,
"segments": segments,
})
)
except Exception as e:
logging.error(f"[ERROR]: Sending data to client: {e}")
def disconnect(self):
"""
Notify the client of disconnection and send a disconnect message.
@@ -306,15 +406,11 @@ class ServeClientBase(object):
that the transcription service is disconnecting gracefully.
"""
self.websocket.send(
json.dumps(
{
"uid": self.client_uid,
"message": self.DISCONNECT
}
)
)
self.websocket.send(json.dumps({
"uid": self.client_uid,
"message": self.DISCONNECT
}))
def cleanup(self):
"""
Perform cleanup tasks before exiting the transcription service.
@@ -357,16 +453,7 @@ class ServeClientTensorRT(ServeClientBase):
pick_previous_segments (int): Number of previous segments to include in the output.
websocket: The WebSocket connection for the client.
"""
def __init__(
self,
websocket,
task="transcribe",
device=None,
multilingual=False,
language=None,
client_uid=None,
model=None
):
def __init__(self, websocket, task="transcribe", multilingual=False, language=None, client_uid=None, model=None):
"""
Initialize a ServeClient instance.
The Whisper model is initialized based on the client's language and device availability.
@@ -387,8 +474,8 @@ class ServeClientTensorRT(ServeClientBase):
self.task = task
self.eos = False
self.transcriber = WhisperTRTLLM(
model,
assets_dir="assets",
model,
assets_dir="assets",
device="cuda",
is_multilingual=multilingual,
language=self.language,
@@ -400,52 +487,44 @@ class ServeClientTensorRT(ServeClientBase):
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,
"backend": "tensorrt"
}
)
)
self.websocket.send(json.dumps({
"uid": self.client_uid,
"message": self.SERVER_READY,
"backend": "tensorrt"
}))
def warmup(self, warmup_steps=10):
logging.info("[INFO:] Warming up TensorRT engine..")
mel, _ = self.transcriber.log_mel_spectrogram("tests/jfk.flac")
for i in range(warmup_steps):
self.transcriber.transcribe(mel)
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.
def handle_transcription_output(self, last_segment, duration):
"""Handle the transcription output, updating the transcript and sending data to the client."""
segments = self.prepare_segments({"text": last_segment})
self.send_transcription_to_client(segments)
if self.eos:
self.update_timestamp_offset(last_segment, duration)
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.
def transcribe_audio(self, input_bytes):
"""Transcribe the audio chunk and send the results to the client."""
logging.info(f"[WhisperTensorRT:] Processing audio with duration: {input_bytes.shape[0] / self.RATE}")
mel, duration = self.transcriber.log_mel_spectrogram(input_bytes)
last_segment = self.transcriber.transcribe(mel)
if last_segment:
self.handle_transcription_output(last_segment, duration)
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 update_timestamp_offset(self, last_segment, 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
def speech_to_text(self):
"""
@@ -456,8 +535,8 @@ class ServeClientTensorRT(ServeClientBase):
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
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:
@@ -468,54 +547,21 @@ class ServeClientTensorRT(ServeClientBase):
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:
self.clip_audio_if_no_valid_segment()
input_bytes, duration = self.get_audio_chunk_for_processing()
if duration < 0.4:
continue
try:
input_sample = input_bytes.copy()
logging.info(f"[WhisperTensorRT:] Processing audio with duration: {duration}")
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()
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:
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
except Exception as e:
logging.error(f"[ERROR]: {e}")
self.transcribe_audio(input_sample)
except Exception as e:
logging.error(f"[ERROR]: {e}")
@@ -550,17 +596,8 @@ class ServeClientFasterWhisper(ServeClientBase):
pick_previous_segments (int): Number of previous segments to include in the output.
websocket: The WebSocket connection for the client.
"""
def __init__(
self,
websocket,
task="transcribe",
device=None,
language=None,
client_uid=None,
model="small.en",
initial_prompt=None,
vad_parameters=None,
):
def __init__(self, websocket, task="transcribe", device=None, language=None, client_uid=None, model="small.en",
initial_prompt=None, vad_parameters=None):
"""
Initialize a ServeClient instance.
The Whisper model is initialized based on the client's language and device availability.
@@ -589,16 +626,16 @@ class ServeClientFasterWhisper(ServeClientBase):
self.initial_prompt = initial_prompt
self.vad_parameters = vad_parameters or {"threshold": 0.5}
self.no_speech_thresh = 0.45
device = "cuda" if torch.cuda.is_available() else "cpu"
if self.model_size_or_path == None:
if self.model_size_or_path is None:
return
self.transcriber = WhisperModel(
self.model_size_or_path,
self.model_size_or_path,
device=device,
compute_type="int8" if device=="cpu" else "float16",
compute_type="int8" if device == "cpu" else "float16",
local_files_only=False,
)
@@ -614,7 +651,7 @@ class ServeClientFasterWhisper(ServeClientBase):
}
)
)
def check_valid_model(self, model_size):
"""
Check if it's a valid whisper model size.
@@ -637,7 +674,39 @@ class ServeClientFasterWhisper(ServeClientBase):
)
return None
return model_size
def set_language(self, info):
if info.language_probability > 0.5:
self.language = info.language
logging.info(f"Detected language {self.language} with probability {info.language_probability}")
self.websocket.send(json.dumps(
{"uid": self.client_uid, "language": self.language, "language_prob": info.language_probability}))
def transcribe_audio(self, input_sample):
result, info = self.transcriber.transcribe(
input_sample,
initial_prompt=self.initial_prompt,
language=self.language,
task=self.task,
vad_filter=True,
vad_parameters=self.vad_parameters)
if self.language is None:
self.set_language(info)
return result
def get_previous_output(self):
segments = []
if self.t_start is None:
self.t_start = time.time()
if time.time() - self.t_start < self.show_prev_out_thresh:
segments = self.prepare_segments()
# add a blank if there is no speech for 3 seconds
if len(self.text) and self.text[-1] != '':
if time.time() - self.t_start > self.add_pause_thresh:
self.text.append('')
return segments
def speech_to_text(self):
"""
Process an audio stream in an infinite loop, continuously transcribing the speech.
@@ -647,8 +716,8 @@ class ServeClientFasterWhisper(ServeClientBase):
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
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:
@@ -659,83 +728,38 @@ class ServeClientFasterWhisper(ServeClientBase):
if self.exit:
logging.info("Exiting speech to text thread")
break
if self.frames_np is None:
if self.frames_np is None:
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<1.0:
self.clip_audio_if_no_valid_segment()
input_bytes, duration = self.get_audio_chunk_for_processing()
if duration < 1.0:
continue
try:
input_sample = input_bytes.copy()
# whisper transcribe with prompt
result, info = self.transcriber.transcribe(
input_sample,
initial_prompt=self.initial_prompt,
language=self.language,
task=self.task,
vad_filter=True,
vad_parameters=self.vad_parameters
)
result = self.transcribe_audio(input_sample)
if self.language is None:
if info.language_probability > 0.5:
self.language = info.language
logging.info(f"Detected language {self.language} with probability {info.language_probability}")
self.websocket.send(json.dumps(
{"uid": self.client_uid, "language": self.language, "language_prob": info.language_probability}))
else:
# detect language again
continue
continue
if len(result):
self.t_start = None
last_segment = self.update_segments(result, duration)
if len(self.transcript) < self.send_last_n_segments:
segments = self.transcript
else:
segments = self.transcript[-self.send_last_n_segments:]
if last_segment is not None:
segments = segments + [last_segment]
segments = self.prepare_segments(last_segment)
else:
# show previous output if there is pause i.e. no output from whisper
segments = []
if self.t_start is None: self.t_start = time.time()
if time.time() - self.t_start < self.show_prev_out_thresh:
if len(self.transcript) < self.send_last_n_segments:
segments = self.transcript
else:
segments = self.transcript[-self.send_last_n_segments:]
# add a blank if there is no speech for 3 seconds
if len(self.text) and self.text[-1] != '':
if time.time() - self.t_start > self.add_pause_thresh:
self.text.append('')
segments = self.get_previous_output()
if not len(segments): continue
try:
self.websocket.send(
json.dumps({
"uid": self.client_uid,
"segments": segments
})
)
except Exception as e:
logging.error(f"[ERROR]: Failed to send message to client: {e}")
if not len(segments):
continue
self.send_transcription_to_client(segments)
except Exception as e:
logging.error(f"[ERROR]: Failed to transcribe audio chunk: {e}")
time.sleep(0.01)
def format_segment(self, start, end, text):
"""Helper function to format a segment with string timestamps."""
return {
@@ -750,17 +774,17 @@ class ServeClientFasterWhisper(ServeClientBase):
except for the last segment assuming that it is incomplete.
Updates the ongoing transcript with transcribed segments, including their start and end times.
Complete segments are appended to the transcript in chronological order. Incomplete segments
(assumed to be the last one) are processed to identify repeated content. If the same incomplete
Complete segments are appended to the transcript in chronological order. Incomplete segments
(assumed to be the last one) are processed to identify repeated content. If the same incomplete
segment is seen multiple times, it updates the offset and appends the segment to the transcript.
A threshold is used to detect repeated content and ensure it is only included once in the transcript.
The timestamp offset is updated based on the duration of processed segments. The method returns the
The timestamp offset is updated based on the duration of processed segments. The method returns the
last processed segment, allowing it to be sent to the client for real-time updates.
Args:
segments(dict) : dictionary of segments as returned by whisper
duration(float): duration of the current chunk
Returns:
dict or None: The last processed segment with its start time, end time, and transcribed text.
Returns None if there are no valid segments to process.
@@ -775,11 +799,12 @@ class ServeClientFasterWhisper(ServeClientBase):
self.text.append(text_)
start, end = self.timestamp_offset + s.start, self.timestamp_offset + min(duration, s.end)
if start >= end: continue
if s.no_speech_prob > self.no_speech_thresh: continue
if start >= end:
continue
if s.no_speech_prob > self.no_speech_thresh:
continue
self.transcript.append(self.format_segment(start, end, text_))
offset = min(duration, s.end)
self.current_out += segments[-1].text
@@ -788,16 +813,16 @@ class ServeClientFasterWhisper(ServeClientBase):
self.timestamp_offset + min(duration, segments[-1].end),
self.current_out
)
# if same incomplete segment is seen multiple times then update the offset
# and append the segment to the list
if self.current_out.strip() == self.prev_out.strip() and self.current_out != '':
if self.current_out.strip() == self.prev_out.strip() and self.current_out != '':
self.same_output_threshold += 1
else:
else:
self.same_output_threshold = 0
if self.same_output_threshold > 5:
if not len(self.text) or self.text[-1].strip().lower()!=self.current_out.strip().lower():
if not len(self.text) or self.text[-1].strip().lower() != self.current_out.strip().lower():
self.text.append(self.current_out)
self.transcript.append(self.format_segment(
self.timestamp_offset,
@@ -810,7 +835,7 @@ class ServeClientFasterWhisper(ServeClientBase):
last_segment = None
else:
self.prev_out = self.current_out
# update offset
if offset is not None:
self.timestamp_offset += offset
whisper_live/tensorrt_utils.py
+2 -2
View File
@@ -214,7 +214,7 @@ def store_transcripts(filename: Pathlike, texts: Iterable[Tuple[str, str,
print(f"{cut_id}:\thyp={hyp}", file=f)
def write_error_stats(
def write_error_stats( # noqa: C901
f: TextIO,
test_set_name: str,
results: List[Tuple[str, str]],
@@ -362,4 +362,4 @@ def write_error_stats(
hyp_count = corr + hyp_sub + ins
print(f"{word} {corr} {tot_errs} {ref_count} {hyp_count}", file=f)
return float(tot_err_rate)
return float(tot_err_rate)
whisper_live/transcriber.py
+4 -4
View File
@@ -400,7 +400,7 @@ class WhisperModel:
return segments, info
def generate_segments(
def generate_segments( # noqa: C901
self,
features: np.ndarray,
tokenizer: Tokenizer,
@@ -425,7 +425,7 @@ class WhisperModel:
all_segments = []
while seek < content_frames:
time_offset = seek * self.feature_extractor.time_per_frame
segment = features[:, seek : seek + self.feature_extractor.nb_max_frames]
segment = features[:, seek:seek + self.feature_extractor.nb_max_frames]
segment_size = min(
self.feature_extractor.nb_max_frames, content_frames - seek
)
@@ -749,7 +749,7 @@ class WhisperModel:
if previous_tokens:
prompt.append(tokenizer.sot_prev)
prompt.extend(previous_tokens[-(self.max_length // 2 - 1) :])
prompt.extend(previous_tokens[-(self.max_length // 2 - 1):])
prompt.extend(tokenizer.sot_sequence)
@@ -766,7 +766,7 @@ class WhisperModel:
return prompt
def add_word_timestamps(
def add_word_timestamps( # noqa: C901
self,
segments: List[dict],
tokenizer: Tokenizer,
whisper_live/transcriber_tensorrt.py
+10 -30
View File
@@ -1,17 +1,14 @@
import argparse
import json
import re
import time
from collections import OrderedDict
from pathlib import Path
from typing import Dict, Iterable, List, Optional, TextIO, Tuple, Union
from typing import Union
import torch
import numpy as np
import torch.nn.functional as F
from whisper.tokenizer import get_tokenizer
from whisper_live.tensorrt_utils import (mel_filters, store_transcripts,
write_error_stats, load_audio_wav_format,
pad_or_trim, load_audio)
from whisper_live.tensorrt_utils import (mel_filters, load_audio_wav_format, pad_or_trim, load_audio)
import tensorrt_llm
import tensorrt_llm.logger as logger
@@ -38,8 +35,6 @@ class WhisperEncoding:
with open(config_path, 'r') as f:
config = json.load(f)
use_gpt_attention_plugin = config['plugin_config'][
'gpt_attention_plugin']
dtype = config['builder_config']['precision']
n_mels = config['builder_config']['n_mels']
num_languages = config['builder_config']['num_languages']
@@ -176,16 +171,8 @@ class WhisperDecoding:
class WhisperTRTLLM(object):
def __init__(
self,
engine_dir,
debug_mode=False,
assets_dir=None,
device=None,
is_multilingual=False,
language="en",
task="transcribe"
):
def __init__(self, engine_dir, assets_dir=None, device=None, is_multilingual=False,
language="en", task="transcribe"):
world_size = 1
runtime_rank = tensorrt_llm.mpi_rank()
runtime_mapping = tensorrt_llm.Mapping(world_size, runtime_rank)
@@ -212,7 +199,7 @@ class WhisperTRTLLM(object):
self,
audio: Union[str, np.ndarray, torch.Tensor],
padding: int = 0,
return_duration = True
return_duration=True
):
"""
Compute the log-Mel spectrogram of
@@ -242,8 +229,7 @@ class WhisperTRTLLM(object):
audio, _ = load_audio_wav_format(audio)
else:
audio = load_audio(audio)
assert isinstance(audio,
np.ndarray), f"Unsupported audio type: {type(audio)}"
assert isinstance(audio, np.ndarray), f"Unsupported audio type: {type(audio)}"
duration = audio.shape[-1] / SAMPLE_RATE
audio = pad_or_trim(audio, N_SAMPLES)
audio = audio.astype(np.float32)
@@ -254,14 +240,9 @@ class WhisperTRTLLM(object):
if padding > 0:
audio = F.pad(audio, (0, padding))
window = torch.hann_window(N_FFT).to(audio.device)
stft = torch.stft(audio,
N_FFT,
HOP_LENGTH,
window=window,
return_complex=True)
stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
magnitudes = stft[..., :-1].abs()**2
mel_spec = self.filters @ magnitudes
log_spec = torch.clamp(mel_spec, min=1e-10).log10()
@@ -272,7 +253,6 @@ class WhisperTRTLLM(object):
else:
return log_spec
def process_batch(
self,
mel,
@@ -296,7 +276,7 @@ class WhisperTRTLLM(object):
text = self.tokenizer.decode(output_ids[i][0]).strip()
texts.append(text)
return texts
def transcribe(
self,
mel,
@@ -336,5 +316,5 @@ def decode_wav_file(
prediction = re.sub(r'<\|.*?\|>', '', prediction)
if normalizer:
prediction = normalizer(prediction)
return prediction.strip()
whisper_live/utils.py
+71
View File
@@ -0,0 +1,71 @@
import os
import textwrap
import scipy
import ffmpeg
import numpy as np
def clear_screen():
"""Clears the console screen."""
os.system("cls" if os.name == "nt" else "clear")
def print_transcript(text):
"""Prints formatted transcript text."""
wrapper = textwrap.TextWrapper(width=60)
for line in wrapper.wrap(text="".join(text)):
print(line)
def format_time(s):
"""Convert seconds (float) to SRT time format."""
hours = int(s // 3600)
minutes = int((s % 3600) // 60)
seconds = int(s % 60)
milliseconds = int((s - int(s)) * 1000)
return f"{hours:02}:{minutes:02}:{seconds:02},{milliseconds:03}"
def create_srt_file(segments, output_file):
with open(output_file, 'w', encoding='utf-8') as srt_file:
segment_number = 1
for segment in segments:
start_time = format_time(float(segment['start']))
end_time = format_time(float(segment['end']))
text = segment['text']
srt_file.write(f"{segment_number}\n")
srt_file.write(f"{start_time} --> {end_time}\n")
srt_file.write(f"{text}\n\n")
segment_number += 1
def resample(file: str, sr: int = 16000):
"""
# https://github.com/openai/whisper/blob/7858aa9c08d98f75575035ecd6481f462d66ca27/whisper/audio.py#L22
Open an audio file and read as mono waveform, resampling as necessary,
save the resampled audio
Args:
file (str): The audio file to open
sr (int): The sample rate to resample the audio if necessary
Returns:
resampled_file (str): The resampled audio file
"""
try:
# This launches a subprocess to decode audio while down-mixing and resampling as necessary.
# Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
out, _ = (
ffmpeg.input(file, threads=0)
.output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
)
except ffmpeg.Error as e:
raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
np_buffer = np.frombuffer(out, dtype=np.int16)
resampled_file = f"{file.split('.')[0]}_resampled.wav"
scipy.io.wavfile.write(resampled_file, sr, np_buffer.astype(np.int16))
return resampled_file
whisper_live/vad.py
+2 -2
View File
@@ -34,7 +34,7 @@ class VoiceActivityDetection():
if sr != 16000 and (sr % 16000 == 0):
step = sr // 16000
x = x[:,::step]
x = x[:, ::step]
sr = 16000
if sr not in self.sample_rates:
@@ -110,4 +110,4 @@ class VoiceActivityDetection():
subprocess.run(["wget", "-O", model_filename, model_url], check=True)
except subprocess.CalledProcessError:
print("Failed to download the model using wget.")
return model_filename
return model_filename