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Merge pull request #146 from makaveli10/code_formatting

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Code formatting
This commit is contained in:
makaveli
2024-02-20 11:32:27 +05:30
committed by GitHub
parent 5e1174ff33 a38fdb494d
commit c919ba3501
15 changed files with 877 additions and 658 deletions
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.github/workflows/ci.yml
+32 -7
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@@ -11,12 +11,11 @@ on:
types: [opened, synchronize, reopened]
jobs:
test:
runs-on: ubuntu-latest
timeout-minutes: 60
run-tests:
runs-on: ubuntu-22.04
strategy:
matrix:
python-version: [3.8, 3.9, '3.10', '3.11']
python-version: [3.8, 3.9, '3.10', 3.11]
steps:
- uses: actions/checkout@v2
@@ -48,10 +47,36 @@ jobs:
run: |
echo "Running tests with Python ${{ matrix.python-version }}"
python -m unittest discover -s tests
check-code-format:
runs-on: ubuntu-22.04
strategy:
matrix:
python-version: [3.8, 3.9, '3.10', 3.11]
build-and-push:
needs: test
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
python-version: ${{ matrix.python-version }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
python -m pip install flake8
- name: Lint with flake8
run: |
# stop the build if there are Python syntax errors or undefined names
flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
# exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
publish-to-pypi:
needs: [run-tests, check-code-format]
runs-on: ubuntu-22.04
if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
steps:
- uses: actions/checkout@v2
run_server.py
+5 -5
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@@ -4,15 +4,15 @@ from whisper_live.server import TranscriptionServer
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--port', '-p',
type=int,
type=int,
default=9090,
help="Websocket port to run the server on.")
parser.add_argument('--backend', '-b',
type=str,
default='faster_whisper',
type=str,
default='faster_whisper',
help='Backends from ["tensorrt", "faster_whisper"]')
parser.add_argument('--faster_whisper_custom_model_path', '-fw',
type=str, default=None,
type=str, default=None,
help="Custom Faster Whisper Model")
parser.add_argument('--trt_model_path', '-trt',
type=str,
@@ -30,7 +30,7 @@ if __name__ == "__main__":
server = TranscriptionServer()
server.run(
"0.0.0.0",
port=args.port,
port=args.port,
backend=args.backend,
faster_whisper_custom_model_path=args.faster_whisper_custom_model_path,
whisper_tensorrt_path=args.trt_model_path,
setup.py
+34 -32
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@@ -10,36 +10,38 @@ HERE = pathlib.Path(__file__).parent
README = (HERE / "README.md").read_text()
# This call to setup() does all the work
setup(name="whisper-live",
version=__version__,
description="A nearly-live implementation of OpenAI's Whisper.",
long_description=README,
long_description_content_type="text/markdown",
include_package_data=True,
url="https://github.com/collabora/WhisperLive",
author="Collabora Ltd",
author_email="vineet.suryan@collabora.com",
license="MIT",
classifiers=[
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
],
packages=find_packages(
exclude=("examples",
"Audio-Transcription-Chrome",
"Audio-Transcription-Firefox",
"requirements",
"whisper-finetuning"
)
),
install_requires=[
setup(
name="whisper-live",
version=__version__,
description="A nearly-live implementation of OpenAI's Whisper.",
long_description=README,
long_description_content_type="text/markdown",
include_package_data=True,
url="https://github.com/collabora/WhisperLive",
author="Collabora Ltd",
author_email="vineet.suryan@collabora.com",
license="MIT",
classifiers=[
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
],
packages=find_packages(
exclude=(
"examples",
"Audio-Transcription-Chrome",
"Audio-Transcription-Firefox",
"requirements",
"whisper-finetuning"
)
),
install_requires=[
"PyAudio",
"faster-whisper==0.10.0",
"torch",
@@ -53,6 +55,6 @@ setup(name="whisper-live",
"openai-whisper",
"kaldialign",
"soundfile",
],
python_requires=">=3.8"
],
python_requires=">=3.8"
)
tests/jfk.flac
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tests/test_client.py
+8 -7
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@@ -4,7 +4,8 @@ import scipy
import websocket
import unittest
from unittest.mock import patch, MagicMock
from whisper_live.client import TranscriptionClient, resample
from whisper_live.client import TranscriptionClient
from whisper_live.utils import resample
class BaseTestCase(unittest.TestCase):
@@ -68,7 +69,7 @@ class TestClientCallbacks(BaseTestCase):
]
})
self.client.on_message(self.mock_ws_app, message)
# Assert that the transcript was updated correctly
self.assertEqual(len(self.client.transcript), 2)
self.assertEqual(self.client.transcript[1]['text'], "Test transcript 2")
@@ -79,14 +80,14 @@ class TestClientCallbacks(BaseTestCase):
self.client.on_close(self.mock_ws_app, close_status_code, close_msg)
self.assertFalse(self.client.recording)
self.assertFalse(self.client.server_error)
self.assertFalse(self.client.server_error)
self.assertFalse(self.client.waiting)
def test_on_error(self):
error_message = "Test Error"
self.client.on_error(self.mock_ws_app, error_message)
self.assertTrue(self.client.server_error)
self.assertTrue(self.client.server_error)
self.assertEqual(self.client.error_message, error_message)
@@ -95,10 +96,10 @@ class TestAudioResampling(unittest.TestCase):
original_audio = "assets/jfk.flac"
expected_sr = 16000
resampled_audio = resample(original_audio, expected_sr)
sr, _ = scipy.io.wavfile.read(resampled_audio)
self.assertEqual(sr, expected_sr)
os.remove(resampled_audio)
tests/test_server.py
+54 -22
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@@ -6,6 +6,8 @@ from unittest import mock
import numpy as np
import evaluate
from websockets.exceptions import ConnectionClosed
from whisper_live.server import TranscriptionServer
from whisper_live.client import TranscriptionClient
from whisper.normalizers import EnglishTextNormalizer
@@ -14,32 +16,31 @@ from whisper.normalizers import EnglishTextNormalizer
class TestTranscriptionServerInitialization(unittest.TestCase):
def test_initialization(self):
server = TranscriptionServer()
self.assertEqual(server.max_clients, 4)
self.assertEqual(server.max_connection_time, 600)
self.assertDictEqual(server.clients, {})
self.assertDictEqual(server.websockets, {})
self.assertDictEqual(server.clients_start_time, {})
self.assertEqual(server.client_manager.max_clients, 4)
self.assertEqual(server.client_manager.max_connection_time, 600)
self.assertDictEqual(server.client_manager.clients, {})
self.assertDictEqual(server.client_manager.start_times, {})
class TestGetWaitTime(unittest.TestCase):
def setUp(self):
self.server = TranscriptionServer()
self.server.clients_start_time = {
self.server.client_manager.start_times = {
'client1': time.time() - 120,
'client2': time.time() - 300
}
self.server.max_connection_time = 600
self.server.client_manager.max_connection_time = 600
def test_get_wait_time(self):
expected_wait_time = (600 - (time.time() - self.server.clients_start_time['client2'])) / 60
print(self.server.get_wait_time(), expected_wait_time)
self.assertAlmostEqual(self.server.get_wait_time(), expected_wait_time, places=2)
expected_wait_time = (600 - (time.time() - self.server.client_manager.start_times['client2'])) / 60
print(self.server.client_manager.get_wait_time(), expected_wait_time)
self.assertAlmostEqual(self.server.client_manager.get_wait_time(), expected_wait_time, places=2)
class TestServerConnection(unittest.TestCase):
def setUp(self):
self.server = TranscriptionServer()
@mock.patch('websockets.WebSocketCommonProtocol')
def test_connection(self, mock_websocket):
mock_websocket.recv.return_value = json.dumps({
@@ -50,7 +51,6 @@ class TestServerConnection(unittest.TestCase):
})
self.server.recv_audio(mock_websocket, "faster_whisper")
@mock.patch('websockets.WebSocketCommonProtocol')
def test_recv_audio_exception_handling(self, mock_websocket):
mock_websocket.recv.side_effect = [json.dumps({
@@ -58,12 +58,12 @@ class TestServerConnection(unittest.TestCase):
'language': 'en',
'task': 'transcribe',
'model': 'tiny.en'
}), np.array([1, 2, 3]).tobytes()]
}), np.array([1, 2, 3]).tobytes()]
with self.assertLogs(level="ERROR"):
self.server.recv_audio(mock_websocket, "faster_whisper")
self.assertNotIn(mock_websocket, self.server.clients)
self.assertNotIn(mock_websocket, self.server.client_manager.clients)
class TestServerInferenceAccuracy(unittest.TestCase):
@@ -71,12 +71,12 @@ class TestServerInferenceAccuracy(unittest.TestCase):
def setUpClass(cls):
cls.server_process = subprocess.Popen(["python", "run_server.py"]) # Adjust the command as needed
time.sleep(2)
@classmethod
def tearDownClass(cls):
cls.server_process.terminate()
cls.server_process.wait()
@mock.patch('pyaudio.PyAudio')
def setUp(self, mock_pyaudio):
self.mock_pyaudio = mock_pyaudio.return_value
@@ -84,16 +84,16 @@ class TestServerInferenceAccuracy(unittest.TestCase):
self.mock_pyaudio.open.return_value = self.mock_stream
self.metric = evaluate.load("wer")
self.normalizer = EnglishTextNormalizer()
self.client = TranscriptionClient(
self.client = TranscriptionClient(
"localhost", "9090", model="base.en", lang="en",
)
def test_inference(self):
gt = "And so my fellow Americans, ask not, what your country can do for you. Ask what you can do for your country!"
self.client("assets/jfk.flac")
with open("output.srt", "r") as f:
lines = f.readlines()
prediction = " ".join([l.strip() for l in lines[2::4]])
prediction = " ".join([line.strip() for line in lines[2::4]])
prediction_normalized = self.normalizer(prediction)
gt_normalized = self.normalizer(gt)
@@ -103,3 +103,35 @@ class TestServerInferenceAccuracy(unittest.TestCase):
references=[gt_normalized]
)
self.assertLess(wer, 0.05)
class TestExceptionHandling(unittest.TestCase):
def setUp(self):
self.server = TranscriptionServer()
@mock.patch('websockets.WebSocketCommonProtocol')
def test_connection_closed_exception(self, mock_websocket):
mock_websocket.recv.side_effect = ConnectionClosed(1001, "testing connection closed")
with self.assertLogs(level="INFO") as log:
self.server.recv_audio(mock_websocket, "faster_whisper")
self.assertTrue(any("Connection closed by client" in message for message in log.output))
@mock.patch('websockets.WebSocketCommonProtocol')
def test_json_decode_exception(self, mock_websocket):
mock_websocket.recv.return_value = "invalid json"
with self.assertLogs(level="ERROR") as log:
self.server.recv_audio(mock_websocket, "faster_whisper")
self.assertTrue(any("Failed to decode JSON from client" in message for message in log.output))
@mock.patch('websockets.WebSocketCommonProtocol')
def test_unexpected_exception_handling(self, mock_websocket):
mock_websocket.recv.side_effect = RuntimeError("Unexpected error")
with self.assertLogs(level="ERROR") as log:
self.server.recv_audio(mock_websocket, "faster_whisper")
for message in log.output:
print(message)
print()
self.assertTrue(any("Unexpected error: Unexpected error" in message for message in log.output))
tests/test_vad.py
+7 -9
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@@ -1,14 +1,12 @@
import unittest
import numpy as np
import torch
import scipy.io as sio
from whisper_live.tensorrt_utils import load_audio
from whisper_live.vad import VoiceActivityDetection
from whisper_live.vad import VoiceActivityDetector
class TestVoiceActivityDetection(unittest.TestCase):
def setUp(self):
self.vad = VoiceActivityDetection()
self.vad = VoiceActivityDetector()
self.sample_rate = 16000
def generate_silence(self, duration_seconds):
@@ -19,10 +17,10 @@ class TestVoiceActivityDetection(unittest.TestCase):
def test_vad_silence_detection(self):
silence = self.generate_silence(3)
speech_prob = self.vad(torch.from_numpy(silence.copy()), self.sample_rate).item()
self.assertLess(speech_prob, 0.5, "VAD incorrectly identified silence as speech.")
is_speech_present = self.vad(silence.copy())
self.assertFalse(is_speech_present, "VAD incorrectly identified silence as speech.")
def test_vad_speech_detection(self):
audio_tensor = torch.from_numpy(load_audio("assets/jfk.flac"))
speech_prob = self.vad(audio_tensor, self.sample_rate).item()
self.assertGreater(speech_prob, 0.5, "VAD failed to identify speech segment.")
audio_tensor = load_audio("assets/jfk.flac")
is_speech_present = self.vad(audio_tensor)
self.assertTrue(is_speech_present, "VAD failed to identify speech segment.")
whisper_live/__version__.py
+1 -1
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@@ -1 +1 @@
__version__="0.1.0"
__version__ = "0.1.0"
whisper_live/client.py
+58 -125
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@@ -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.
@@ -171,18 +147,11 @@ 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":
print("[INFO]: Server overtime disconnected.")
print("[INFO]: Server disconnected due to overtime.")
self.recording = False
if "message" in message.keys() and message["message"] == "SERVER_READY":
@@ -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
+560 -412
View File
File diff suppressed because it is too large Load Diff
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
+31 -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 +110,33 @@ 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
class VoiceActivityDetector:
def __init__(self, threshold=0.5, frame_rate=16000):
"""
Initializes the VoiceActivityDetector with a voice activity detection model and a threshold.
Args:
threshold (float, optional): The probability threshold for detecting voice activity. Defaults to 0.5.
"""
self.model = VoiceActivityDetection()
self.threshold = threshold
self.frame_rate = frame_rate
def __call__(self, audio_frame):
"""
Determines if the given audio frame contains speech by comparing the detected speech probability against
the threshold.
Args:
audio_frame (np.ndarray): The audio frame to be analyzed for voice activity. It is expected to be a
NumPy array of audio samples.
Returns:
bool: True if the speech probability exceeds the threshold, indicating the presence of voice activity;
False otherwise.
"""
speech_prob = self.model(torch.from_numpy(audio_frame), self.frame_rate).item()
return speech_prob > self.threshold