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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
@@ -49,9 +48,35 @@ jobs:
echo "Running tests with Python ${{ matrix.python-version }}"
python -m unittest discover -s tests
build-and-push:
needs: test
runs-on: ubuntu-latest
check-code-format:
runs-on: ubuntu-22.04
strategy:
matrix:
python-version: [3.8, 3.9, '3.10', 3.11]
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
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
BIN
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tests/test_client.py
+2 -1
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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):
tests/test_server.py
+46 -14
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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
@@ -14,26 +16,25 @@ 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):
@@ -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({
@@ -63,7 +63,7 @@ class TestServerConnection(unittest.TestCase):
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):
@@ -84,7 +84,7 @@ 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",
)
@@ -93,7 +93,7 @@ class TestServerInferenceAccuracy(unittest.TestCase):
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
+42 -109
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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:
@@ -150,6 +96,36 @@ 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.
@@ -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}")
@@ -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.
@@ -448,7 +386,8 @@ class Client:
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)
@@ -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):
@@ -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()
whisper_live/server.py
+523 -375
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File diff suppressed because it is too large Load Diff
whisper_live/tensorrt_utils.py
+1 -1
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@@ -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]],
whisper_live/transcriber.py
+4 -4
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@@ -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
+8 -28
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@@ -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,
whisper_live/utils.py
+71
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@@ -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
+30 -1
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:
@@ -111,3 +111,32 @@ class VoiceActivityDetection():
except subprocess.CalledProcessError:
print("Failed to download the model using wget.")
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