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Merge pull request #348 from makaveli10/integrate_openvino

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Integrate openvino
This commit is contained in:
makaveli
2025-04-08 23:31:53 +05:30
committed by GitHub
parent f5bea0a693 a62495b090
commit 2abca69c9d
18 changed files with 1066 additions and 754 deletions
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.github/workflows/ci.yml
+37 -8
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@@ -15,7 +15,7 @@ jobs:
runs-on: ubuntu-22.04
strategy:
matrix:
python-version: [3.8, 3.9, '3.10', 3.11, 3.12]
python-version: [3.9, '3.10', 3.11, 3.12]
steps:
- uses: actions/checkout@v2
@@ -25,7 +25,7 @@ jobs:
python-version: ${{ matrix.python-version }}
- name: Cache Python dependencies
uses: actions/cache@v2
uses: actions/cache@v4
with:
path: |
~/.cache/pip
@@ -52,7 +52,7 @@ jobs:
runs-on: ubuntu-22.04
strategy:
matrix:
python-version: [3.8, 3.9, '3.10', 3.11, 3.12]
python-version: [3.9, '3.10', 3.11, 3.12]
steps:
- uses: actions/checkout@v2
@@ -157,6 +157,35 @@ jobs:
push: true
tags: ghcr.io/collabora/whisperlive-gpu:latest
build-and-push-docker-openvino:
needs: [run-tests, check-code-format, build-and-push-docker-cpu]
timeout-minutes: 20
runs-on: ubuntu-22.04
if: github.event_name == 'push' && (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/tags/'))
steps:
- uses: actions/checkout@v2
- name: Log in to GitHub Container Registry
uses: docker/login-action@v1
with:
registry: ghcr.io
username: ${{ github.repository_owner }}
password: ${{ secrets.GHCR_TOKEN }}
- name: Docker Prune
run: docker system prune -af
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v1
- name: Build and push Docker GPU image
uses: docker/build-push-action@v2
with:
context: .
file: docker/Dockerfile.openvino
push: true
tags: ghcr.io/collabora/whisperlive-openvino:latest
publish-to-pypi:
needs: [run-tests, check-code-format]
runs-on: ubuntu-22.04
@@ -164,20 +193,20 @@ jobs:
steps:
- uses: actions/checkout@v2
- name: Set up Python 3.8
- name: Set up Python 3.9
uses: actions/setup-python@v2
with:
python-version: 3.8
python-version: 3.9
- name: Cache Python dependencies
uses: actions/cache@v2
uses: actions/cache@v4
with:
path: |
~/.cache/pip
!~/.cache/pip/log
key: ubuntu-latest-pip-3.8-${{ hashFiles('requirements/server.txt', 'requirements/client.txt') }}
key: ubuntu-latest-pip-3.9-${{ hashFiles('requirements/server.txt', 'requirements/client.txt') }}
restore-keys: |
ubuntu-latest-pip-3.8-
ubuntu-latest-pip-3.9-
- name: Install system dependencies
run: sudo apt-get update && sudo apt-get install -y portaudio19-dev
README.md
+31 -5
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@@ -11,6 +11,16 @@ This project is a real-time transcription application that uses the OpenAI Whisp
to convert speech input into text output. It can be used to transcribe both live audio
input from microphone and pre-recorded audio files.
- [Installation](#installation)
- [Getting Started](#getting-started)
- [Running the Server](#running-the-server)
- [Running the Client](#running-the-client)
- [Browser Extensions](#browser-extensions)
- [Whisper Live Server in Docker](#whisper-live-server-in-docker)
- [Future Work](#future-work)
- [Contact](#contact)
- [Citations](#citations)
## Installation
- Install PyAudio
```bash
@@ -26,7 +36,7 @@ input from microphone and pre-recorded audio files.
- Please follow [TensorRT_whisper readme](https://github.com/collabora/WhisperLive/blob/main/TensorRT_whisper.md) for setup of [NVIDIA/TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) and for building Whisper-TensorRT engine.
## Getting Started
The server supports two backends `faster_whisper` and `tensorrt`. If running `tensorrt` backend follow [TensorRT_whisper readme](https://github.com/collabora/WhisperLive/blob/main/TensorRT_whisper.md)
The server supports 3 backends `faster_whisper`, `tensorrt` and `openvino`. If running `tensorrt` backend follow [TensorRT_whisper readme](https://github.com/collabora/WhisperLive/blob/main/TensorRT_whisper.md)
### Running the Server
- [Faster Whisper](https://github.com/SYSTRAN/faster-whisper) backend
@@ -53,6 +63,16 @@ python3 run_server.py -p 9090 \
-trt /home/TensorRT-LLM/examples/whisper/whisper_small \
-m
```
- WhisperLive now supports the [OpenVINO](https://github.com/openvinotoolkit/openvino) backend for efficient inference on Intel CPUs, iGPU and dGPUs. Currently, we tested the models uploaded to [huggingface by OpenVINO](https://huggingface.co/OpenVINO?search_models=whisper).
- > **Docker Recommended:** Running WhisperLive with OpenVINO inside Docker automatically enables GPU support (iGPU/dGPU) without requiring additional host setup.
- > **Native (non-Docker) Use:** If you prefer running outside Docker, ensure the Intel drivers and OpenVINO runtime are installed and properly configured on your system. Refer to the documentation for [installing OpenVINO](https://docs.openvino.ai/2025/get-started/install-openvino.html?PACKAGE=OPENVINO_BASE&VERSION=v_2025_0_0&OP_SYSTEM=LINUX&DISTRIBUTION=PIP#).
```
python3 run_server.py -p 9090 -b openvino
```
#### Controlling OpenMP Threads
To control the number of threads used by OpenMP, you can set the `OMP_NUM_THREADS` environment variable. This is useful for managing CPU resources and ensuring consistent performance. If not specified, `OMP_NUM_THREADS` is set to `1` by default. You can change this by using the `--omp_num_threads` argument:
```bash
@@ -147,15 +167,21 @@ client(hls_url="http://as-hls-ww-live.akamaized.net/pool_904/live/ww/bbc_1xtra/b
--trt_model_path "/app/TensorRT-LLM-examples/whisper/whisper_small_en_int4"
```
- OpenVINO
```
docker run -it --device=/dev/dri -p 9090:9090 ghcr.io/collabora/whisperlive-openvino
```
- CPU
```bash
docker run -it -p 9090:9090 ghcr.io/collabora/whisperlive-cpu:latest
```
- Faster-whisper
```bash
docker run -it -p 9090:9090 ghcr.io/collabora/whisperlive-cpu:latest
```
**Note**: By default we use "small" model size. To build docker image for a different model size, change the size in server.py and then build the docker image.
## Future Work
- [ ] Add translation to other languages on top of transcription.
- [x] TensorRT backend for Whisper.
## Contact
docker/Dockerfile.openvino
+19
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@@ -0,0 +1,19 @@
FROM openvino/ubuntu22_runtime:latest
ARG DEBIAN_FRONTEND=noninteractive
USER root
RUN apt update && apt install -y portaudio19-dev python-is-python3 && apt-get clean && rm -rf /var/lib/apt/lists/*
RUN pip install --no-cache-dir -U "pip>=24"
RUN mkdir /app
WORKDIR /app
COPY requirements/server.txt /app/
RUN pip install --no-cache-dir -r server.txt && rm server.txt
COPY whisper_live /app/whisper_live
COPY run_server.py /app
CMD ["python", "run_server.py", "--backend", "openvino"]
requirements/server.txt
+9 -1
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@@ -10,4 +10,12 @@ jiwer
evaluate
numpy<2
openai-whisper==20240930
tokenizers==0.20.3
tokenizers==0.20.3
# openvino
librosa
openvino
openvino-genai
openvino-tokenizers
optimum
optimum-intel
run_server.py
+1 -1
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@@ -10,7 +10,7 @@ if __name__ == "__main__":
parser.add_argument('--backend', '-b',
type=str,
default='faster_whisper',
help='Backends from ["tensorrt", "faster_whisper"]')
help='Backends from ["tensorrt", "faster_whisper", "openvino"]')
parser.add_argument('--faster_whisper_custom_model_path', '-fw',
type=str, default=None,
help="Custom Faster Whisper Model")
setup.py
+9 -2
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@@ -54,7 +54,14 @@ setup(
"openai-whisper==20240930",
"kaldialign",
"soundfile",
"tokenizers==0.20.3"
"tokenizers==0.20.3",
"librosa",
"numpy==1.26.4"
"openvino",
"openvino-genai",
"openvino-tokenizers",
"optimum",
"optimum-intel",
],
python_requires=">=3.8"
python_requires=">=3.9"
)
tests/test_vad.py
+1 -1
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@@ -1,6 +1,6 @@
import unittest
import numpy as np
from whisper_live.tensorrt_utils import load_audio
from whisper_live.transcriber.tensorrt_utils import load_audio
from whisper_live.vad import VoiceActivityDetector
whisper_live/backend/__init__.py
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whisper_live/backend/base.py
+370
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@@ -0,0 +1,370 @@
import json
import logging
import threading
import time
import numpy as np
class ServeClientBase(object):
RATE = 16000
SERVER_READY = "SERVER_READY"
DISCONNECT = "DISCONNECT"
def __init__(self, client_uid, websocket):
self.client_uid = client_uid
self.websocket = websocket
self.frames = b""
self.timestamp_offset = 0.0
self.frames_np = None
self.frames_offset = 0.0
self.text = []
self.current_out = ''
self.prev_out = ''
self.t_start = None
self.exit = False
self.same_output_count = 0
self.show_prev_out_thresh = 5 # if pause(no output from whisper) show previous output for 5 seconds
self.add_pause_thresh = 3 # add a blank to segment list as a pause(no speech) for 3 seconds
self.transcript = []
self.send_last_n_segments = 10
self.no_speech_thresh = 0.45
# text formatting
self.pick_previous_segments = 2
# threading
self.lock = threading.Lock()
def speech_to_text(self):
"""
Process an audio stream in an infinite loop, continuously transcribing the speech.
This method continuously receives audio frames, performs real-time transcription, and sends
transcribed segments to the client via a WebSocket connection.
If the client's language is not detected, it waits for 30 seconds of audio input to make a language prediction.
It utilizes the Whisper ASR model to transcribe the audio, continuously processing and streaming results. Segments
are sent to the client in real-time, and a history of segments is maintained to provide context.Pauses in speech
(no output from Whisper) are handled by showing the previous output for a set duration. A blank segment is added if
there is no speech for a specified duration to indicate a pause.
Raises:
Exception: If there is an issue with audio processing or WebSocket communication.
"""
while True:
if self.exit:
logging.info("Exiting speech to text thread")
break
if self.frames_np is None:
continue
self.clip_audio_if_no_valid_segment()
input_bytes, duration = self.get_audio_chunk_for_processing()
if duration < 1.0:
time.sleep(0.1) # wait for audio chunks to arrive
continue
try:
input_sample = input_bytes.copy()
result = self.transcribe_audio(input_sample)
if result is None or self.language is None:
self.timestamp_offset += duration
time.sleep(0.25) # wait for voice activity, result is None when no voice activity
continue
self.handle_transcription_output(result, duration)
except Exception as e:
logging.error(f"[ERROR]: Failed to transcribe audio chunk: {e}")
time.sleep(0.01)
def transcribe_audio(self):
raise NotImplementedError
def handle_transcription_output(self):
raise NotImplementedError
def format_segment(self, start, end, text, completed=False):
"""
Formats a transcription segment with precise start and end times alongside the transcribed text.
Args:
start (float): The start time of the transcription segment in seconds.
end (float): The end time of the transcription segment in seconds.
text (str): The transcribed text corresponding to the segment.
Returns:
dict: A dictionary representing the formatted transcription segment, including
'start' and 'end' times as strings with three decimal places and the 'text'
of the transcription.
"""
return {
'start': "{:.3f}".format(start),
'end': "{:.3f}".format(end),
'text': text,
'completed': completed
}
def add_frames(self, frame_np):
"""
Add audio frames to the ongoing audio stream buffer.
This method is responsible for maintaining the audio stream buffer, allowing the continuous addition
of audio frames as they are received. It also ensures that the buffer does not exceed a specified size
to prevent excessive memory usage.
If the buffer size exceeds a threshold (45 seconds of audio data), it discards the oldest 30 seconds
of audio data to maintain a reasonable buffer size. If the buffer is empty, it initializes it with the provided
audio frame. The audio stream buffer is used for real-time processing of audio data for transcription.
Args:
frame_np (numpy.ndarray): The audio frame data as a NumPy array.
"""
self.lock.acquire()
if self.frames_np is not None and self.frames_np.shape[0] > 45*self.RATE:
self.frames_offset += 30.0
self.frames_np = self.frames_np[int(30*self.RATE):]
# check timestamp offset(should be >= self.frame_offset)
# this basically means that there is no speech as timestamp offset hasnt updated
# and is less than frame_offset
if self.timestamp_offset < self.frames_offset:
self.timestamp_offset = self.frames_offset
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 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
"""
with self.lock:
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):
"""
Retrieves the next chunk of audio data for processing based on the current offsets.
Calculates which part of the audio data should be processed next, based on
the difference between the current timestamp offset and the frame's offset, scaled by
the audio sample rate (RATE). It then returns this chunk of audio data along with its
duration in seconds.
Returns:
tuple: A tuple containing:
- input_bytes (np.ndarray): The next chunk of audio data to be processed.
- duration (float): The duration of the audio chunk in seconds.
"""
with self.lock:
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):
"""
Prepares the segments of transcribed text to be sent to the client.
This method compiles the recent segments of transcribed text, ensuring that only the
specified number of the most recent segments are included. It also appends the most
recent segment of text if provided (which is considered incomplete because of the possibility
of the last word being truncated in the audio chunk).
Args:
last_segment (str, optional): The most recent segment of transcribed text to be added
to the list of segments. Defaults to None.
Returns:
list: A list of transcribed text 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):
"""
Calculates the duration of the provided audio chunk.
Args:
input_bytes (numpy.ndarray): The audio chunk for which to calculate the duration.
Returns:
float: The duration of the audio chunk in seconds.
"""
return input_bytes.shape[0] / self.RATE
def send_transcription_to_client(self, segments):
"""
Sends the specified transcription segments to the client over the websocket connection.
This method formats the transcription segments into a JSON object and attempts to send
this object to the client. If an error occurs during the send operation, it logs the error.
Returns:
segments (list): A list of transcription segments to be sent 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 get_previous_output(self):
"""
Retrieves previously generated transcription outputs if no new transcription is available
from the current audio chunks.
Checks the time since the last transcription output and, if it is within a specified
threshold, returns the most recent segments of transcribed text. It also manages
adding a pause (blank segment) to indicate a significant gap in speech based on a defined
threshold.
Returns:
segments (list): A list of transcription segments. This may include the most recent
transcribed text segments or a blank segment to indicate a pause
in speech.
"""
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 disconnect(self):
"""
Notify the client of disconnection and send a disconnect message.
This method sends a disconnect message to the client via the WebSocket connection to notify them
that the transcription service is disconnecting gracefully.
"""
self.websocket.send(json.dumps({
"uid": self.client_uid,
"message": self.DISCONNECT
}))
def cleanup(self):
"""
Perform cleanup tasks before exiting the transcription service.
This method performs necessary cleanup tasks, including stopping the transcription thread, marking
the exit flag to indicate the transcription thread should exit gracefully, and destroying resources
associated with the transcription process.
"""
logging.info("Cleaning up.")
self.exit = True
def get_segment_no_speech_prob(self, segment):
return getattr(segment, "no_speech_prob", 0)
def get_segment_start(self, segment):
return getattr(segment, "start", getattr(segment, "start_ts", 0))
def get_segment_end(self, segment):
return getattr(segment, "end", getattr(segment, "end_ts", 0))
def update_segments(self, segments, duration):
"""
Processes the segments from Whisper and updates the transcript.
Uses helper methods to account for differences between backends.
Args:
segments (list): List of segments returned by the transcriber.
duration (float): Duration of the current audio chunk.
Returns:
dict or None: The last processed segment (if any).
"""
offset = None
self.current_out = ''
last_segment = None
# Process complete segments only if there are more than one
# and if the last segment's no_speech_prob is below the threshold.
if len(segments) > 1 and self.get_segment_no_speech_prob(segments[-1]) <= self.no_speech_thresh:
for s in segments[:-1]:
text_ = s.text
self.text.append(text_)
with self.lock:
start = self.timestamp_offset + self.get_segment_start(s)
end = self.timestamp_offset + min(duration, self.get_segment_end(s))
if start >= end:
continue
if self.get_segment_no_speech_prob(s) > self.no_speech_thresh:
continue
self.transcript.append(self.format_segment(start, end, text_, completed=True))
offset = min(duration, self.get_segment_end(s))
# Process the last segment if its no_speech_prob is acceptable.
if self.get_segment_no_speech_prob(segments[-1]) <= self.no_speech_thresh:
self.current_out += segments[-1].text
with self.lock:
last_segment = self.format_segment(
self.timestamp_offset + self.get_segment_start(segments[-1]),
self.timestamp_offset + min(duration, self.get_segment_end(segments[-1])),
self.current_out,
completed=False
)
# Handle repeated output logic.
if self.current_out.strip() == self.prev_out.strip() and self.current_out != '':
self.same_output_count += 1
# if we remove the audio because of same output on the nth reptition we might remove the
# audio thats not yet transcribed so, capturing the time when it was repeated for the first time
if self.end_time_for_same_output is None:
self.end_time_for_same_output = self.get_segment_end(segments[-1])
time.sleep(0.1) # wait briefly for any new voice activity
else:
self.same_output_count = 0
self.end_time_for_same_output = None
# If the same incomplete segment is repeated too many times,
# append it to the transcript and update the offset.
if self.same_output_count > self.same_output_threshold:
if not self.text or self.text[-1].strip().lower() != self.current_out.strip().lower():
self.text.append(self.current_out)
with self.lock:
self.transcript.append(self.format_segment(
self.timestamp_offset,
self.timestamp_offset + min(duration, self.end_time_for_same_output),
self.current_out,
completed=True
))
self.current_out = ''
offset = min(duration, self.end_time_for_same_output)
self.same_output_count = 0
last_segment = None
self.end_time_for_same_output = None
else:
self.prev_out = self.current_out
if offset is not None:
with self.lock:
self.timestamp_offset += offset
return last_segment
whisper_live/backend/faster_whisper_backend.py
+223
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@@ -0,0 +1,223 @@
import json
import logging
import threading
import time
import torch
from whisper_live.transcriber.transcriber_faster_whisper import WhisperModel
from whisper_live.backend.base import ServeClientBase
class ServeClientFasterWhisper(ServeClientBase):
SINGLE_MODEL = None
SINGLE_MODEL_LOCK = threading.Lock()
def __init__(self, websocket, task="transcribe", device=None, language=None, client_uid=None, model="small.en",
initial_prompt=None, vad_parameters=None, use_vad=True, single_model=False):
"""
Initialize a ServeClient instance.
The Whisper model is initialized based on the client's language and device availability.
The transcription thread is started upon initialization. A "SERVER_READY" message is sent
to the client to indicate that the server is ready.
Args:
websocket (WebSocket): The WebSocket connection for the client.
task (str, optional): The task type, e.g., "transcribe." Defaults to "transcribe".
device (str, optional): The device type for Whisper, "cuda" or "cpu". Defaults to None.
language (str, optional): The language for transcription. Defaults to None.
client_uid (str, optional): A unique identifier for the client. Defaults to None.
model (str, optional): The whisper model size. Defaults to 'small.en'
initial_prompt (str, optional): Prompt for whisper inference. Defaults to None.
single_model (bool, optional): Whether to instantiate a new model for each client connection. Defaults to False.
"""
super().__init__(client_uid, websocket)
self.model_sizes = [
"tiny", "tiny.en", "base", "base.en", "small", "small.en",
"medium", "medium.en", "large-v2", "large-v3", "distil-small.en",
"distil-medium.en", "distil-large-v2", "distil-large-v3",
"large-v3-turbo", "turbo"
]
self.model_size_or_path = model
self.language = "en" if self.model_size_or_path.endswith("en") else language
self.task = task
self.initial_prompt = initial_prompt
self.vad_parameters = vad_parameters or {"onset": 0.5}
self.same_output_threshold = 10
self.end_time_for_same_output = None
device = "cuda" if torch.cuda.is_available() else "cpu"
if device == "cuda":
major, _ = torch.cuda.get_device_capability(device)
self.compute_type = "float16" if major >= 7 else "float32"
else:
self.compute_type = "int8"
if self.model_size_or_path is None:
return
logging.info(f"Using Device={device} with precision {self.compute_type}")
try:
if single_model:
if ServeClientFasterWhisper.SINGLE_MODEL is None:
self.create_model(device)
ServeClientFasterWhisper.SINGLE_MODEL = self.transcriber
else:
self.transcriber = ServeClientFasterWhisper.SINGLE_MODEL
else:
self.create_model(device)
except Exception as e:
logging.error(f"Failed to load model: {e}")
self.websocket.send(json.dumps({
"uid": self.client_uid,
"status": "ERROR",
"message": f"Failed to load model: {str(self.model_size_or_path)}"
}))
self.websocket.close()
return
self.use_vad = use_vad
# threading
self.trans_thread = threading.Thread(target=self.speech_to_text)
self.trans_thread.start()
self.websocket.send(
json.dumps(
{
"uid": self.client_uid,
"message": self.SERVER_READY,
"backend": "faster_whisper"
}
)
)
def create_model(self, device):
"""
Instantiates a new model, sets it as the transcriber.
"""
self.transcriber = WhisperModel(
self.model_size_or_path,
device=device,
compute_type=self.compute_type,
local_files_only=False,
)
def check_valid_model(self, model_size):
"""
Check if it's a valid whisper model size.
Args:
model_size (str): The name of the model size to check.
Returns:
str: The model size if valid, None otherwise.
"""
if model_size not in self.model_sizes:
self.websocket.send(
json.dumps(
{
"uid": self.client_uid,
"status": "ERROR",
"message": f"Invalid model size {model_size}. Available choices: {self.model_sizes}"
}
)
)
return None
return model_size
def set_language(self, info):
"""
Updates the language attribute based on the detected language information.
Args:
info (object): An object containing the detected language and its probability. This object
must have at least two attributes: `language`, a string indicating the detected
language, and `language_probability`, a float representing the confidence level
of the language detection.
"""
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):
"""
Transcribes the provided audio sample using the configured transcriber instance.
If the language has not been set, it updates the session's language based on the transcription
information.
Args:
input_sample (np.array): The audio chunk to be transcribed. This should be a NumPy
array representing the audio data.
Returns:
The transcription result from the transcriber. The exact format of this result
depends on the implementation of the `transcriber.transcribe` method but typically
includes the transcribed text.
"""
if ServeClientFasterWhisper.SINGLE_MODEL:
ServeClientFasterWhisper.SINGLE_MODEL_LOCK.acquire()
result, info = self.transcriber.transcribe(
input_sample,
initial_prompt=self.initial_prompt,
language=self.language,
task=self.task,
vad_filter=self.use_vad,
vad_parameters=self.vad_parameters if self.use_vad else None)
if ServeClientFasterWhisper.SINGLE_MODEL:
ServeClientFasterWhisper.SINGLE_MODEL_LOCK.release()
if self.language is None and info is not None:
self.set_language(info)
return result
def get_previous_output(self):
"""
Retrieves previously generated transcription outputs if no new transcription is available
from the current audio chunks.
Checks the time since the last transcription output and, if it is within a specified
threshold, returns the most recent segments of transcribed text. It also manages
adding a pause (blank segment) to indicate a significant gap in speech based on a defined
threshold.
Returns:
segments (list): A list of transcription segments. This may include the most recent
transcribed text segments or a blank segment to indicate a pause
in speech.
"""
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 handle_transcription_output(self, result, duration):
"""
Handle the transcription output, updating the transcript and sending data to the client.
Args:
result (str): The result from whisper inference i.e. the list of segments.
duration (float): Duration of the transcribed audio chunk.
"""
segments = []
if len(result):
self.t_start = None
last_segment = self.update_segments(result, duration)
segments = self.prepare_segments(last_segment)
else:
# show previous output if there is pause i.e. no output from whisper
segments = self.get_previous_output()
if len(segments):
self.send_transcription_to_client(segments)
whisper_live/backend/openvino_backend.py
+125
View File
@@ -0,0 +1,125 @@
import json
import logging
import threading
import time
from openvino import Core
from whisper_live.backend.base import ServeClientBase
from whisper_live.transcriber.transcriber_openvino import WhisperOpenVINO
class ServeClientOpenVINO(ServeClientBase):
SINGLE_MODEL = None
SINGLE_MODEL_LOCK = threading.Lock()
def __init__(self, websocket, task="transcribe", device=None, language=None, client_uid=None, model="small.en",
initial_prompt=None, vad_parameters=None, use_vad=True, single_model=False):
"""
Initialize a ServeClient instance.
The Whisper model is initialized based on the client's language and device availability.
The transcription thread is started upon initialization. A "SERVER_READY" message is sent
to the client to indicate that the server is ready.
Args:
websocket (WebSocket): The WebSocket connection for the client.
task (str, optional): The task type, e.g., "transcribe." Defaults to "transcribe".
device (str, optional): The device type for Whisper, "cuda" or "cpu". Defaults to None.
language (str, optional): The language for transcription. Defaults to None.
client_uid (str, optional): A unique identifier for the client. Defaults to None.
model (str, optional): Huggingface model_id for a valid OpenVINO model.
initial_prompt (str, optional): Prompt for whisper inference. Defaults to None.
single_model (bool, optional): Whether to instantiate a new model for each client connection. Defaults to False.
"""
super().__init__(client_uid, websocket)
self.language = "en" if language is None else language
if not self.language.startswith("<|"):
self.language = f"<|{self.language}|>"
self.task = "transcribe" if task is None else task
self.same_output_threshold = 10
self.end_time_for_same_output = None
core = Core()
available_devices = core.available_devices
if 'GPU' in available_devices:
selected_device = 'GPU'
else:
gpu_devices = [d for d in available_devices if d.startswith('GPU')]
selected_device = gpu_devices[0] if gpu_devices else 'CPU'
self.device = selected_device
if single_model:
if ServeClientOpenVINO.SINGLE_MODEL is None:
self.create_model(model)
ServeClientOpenVINO.SINGLE_MODEL = self.transcriber
else:
self.transcriber = ServeClientOpenVINO.SINGLE_MODEL
else:
self.create_model(model)
# threading
self.trans_thread = threading.Thread(target=self.speech_to_text)
self.trans_thread.start()
self.websocket.send(json.dumps({
"uid": self.client_uid,
"message": self.SERVER_READY,
"backend": "openvino"
}))
logging.info(f"Using OpenVINO device: {self.device}")
logging.info(f"Running OpenVINO backend with language: {self.language} and task: {self.task}")
def create_model(self, model_id):
"""
Instantiates a new model, sets it as the transcriber.
"""
self.transcriber = WhisperOpenVINO(
model_id,
device=self.device,
language=self.language,
task=self.task
)
def transcribe_audio(self, input_sample):
"""
Transcribes the provided audio sample using the configured transcriber instance.
If the language has not been set, it updates the session's language based on the transcription
information.
Args:
input_sample (np.array): The audio chunk to be transcribed. This should be a NumPy
array representing the audio data.
Returns:
The transcription result from the transcriber. The exact format of this result
depends on the implementation of the `transcriber.transcribe` method but typically
includes the transcribed text.
"""
if ServeClientOpenVINO.SINGLE_MODEL:
ServeClientOpenVINO.SINGLE_MODEL_LOCK.acquire()
result = self.transcriber.transcribe(input_sample)
if ServeClientOpenVINO.SINGLE_MODEL:
ServeClientOpenVINO.SINGLE_MODEL_LOCK.release()
return result
def handle_transcription_output(self, result, duration):
"""
Handle the transcription output, updating the transcript and sending data to the client.
Args:
result (str): The result from whisper inference i.e. the list of segments.
duration (float): Duration of the transcribed audio chunk.
"""
segments = []
if len(result):
self.t_start = None
last_segment = self.update_segments(result, duration)
segments = self.prepare_segments(last_segment)
else:
# show previous output if there is pause i.e. no output from whisper
segments = self.get_previous_output()
if len(segments):
self.send_transcription_to_client(segments)
whisper_live/backend/trt_backend.py
+181
View File
@@ -0,0 +1,181 @@
import json
import logging
import threading
import time
from whisper_live.backend.base import ServeClientBase
from whisper_live.transcriber.transcriber_tensorrt import WhisperTRTLLM
class ServeClientTensorRT(ServeClientBase):
SINGLE_MODEL = None
SINGLE_MODEL_LOCK = threading.Lock()
def __init__(self, websocket, task="transcribe", multilingual=False, language=None, client_uid=None, model=None, single_model=False):
"""
Initialize a ServeClient instance.
The Whisper model is initialized based on the client's language and device availability.
The transcription thread is started upon initialization. A "SERVER_READY" message is sent
to the client to indicate that the server is ready.
Args:
websocket (WebSocket): The WebSocket connection for the client.
task (str, optional): The task type, e.g., "transcribe." Defaults to "transcribe".
device (str, optional): The device type for Whisper, "cuda" or "cpu". Defaults to None.
multilingual (bool, optional): Whether the client supports multilingual transcription. Defaults to False.
language (str, optional): The language for transcription. Defaults to None.
client_uid (str, optional): A unique identifier for the client. Defaults to None.
single_model (bool, optional): Whether to instantiate a new model for each client connection. Defaults to False.
"""
super().__init__(client_uid, websocket)
self.language = language if multilingual else "en"
self.task = task
self.eos = False
if single_model:
if ServeClientTensorRT.SINGLE_MODEL is None:
self.create_model(model, multilingual)
ServeClientTensorRT.SINGLE_MODEL = self.transcriber
else:
self.transcriber = ServeClientTensorRT.SINGLE_MODEL
else:
self.create_model(model, multilingual)
# threading
self.trans_thread = threading.Thread(target=self.speech_to_text)
self.trans_thread.start()
self.websocket.send(json.dumps({
"uid": self.client_uid,
"message": self.SERVER_READY,
"backend": "tensorrt"
}))
def create_model(self, model, multilingual, warmup=True):
"""
Instantiates a new model, sets it as the transcriber and does warmup if desired.
"""
self.transcriber = WhisperTRTLLM(
model,
assets_dir="assets",
device="cuda",
is_multilingual=multilingual,
language=self.language,
task=self.task
)
if warmup:
self.warmup()
def warmup(self, warmup_steps=10):
"""
Warmup TensorRT since first few inferences are slow.
Args:
warmup_steps (int): Number of steps to warm up the model for.
"""
logging.info("[INFO:] Warming up TensorRT engine..")
mel, _ = self.transcriber.log_mel_spectrogram("assets/jfk.flac")
for i in range(warmup_steps):
self.transcriber.transcribe(mel)
def set_eos(self, eos):
"""
Sets the End of Speech (EOS) flag.
Args:
eos (bool): The value to set for the EOS flag.
"""
self.lock.acquire()
self.eos = eos
self.lock.release()
def handle_transcription_output(self, last_segment, duration):
"""
Handle the transcription output, updating the transcript and sending data to the client.
Args:
last_segment (str): The last segment from the whisper output which is considered to be incomplete because
of the possibility of word being truncated.
duration (float): Duration of the transcribed audio chunk.
"""
segments = self.prepare_segments({"text": last_segment})
self.send_transcription_to_client(segments)
if self.eos:
self.update_timestamp_offset(last_segment, duration)
def transcribe_audio(self, input_bytes):
"""
Transcribe the audio chunk and send the results to the client.
Args:
input_bytes (np.array): The audio chunk to transcribe.
"""
if ServeClientTensorRT.SINGLE_MODEL:
ServeClientTensorRT.SINGLE_MODEL_LOCK.acquire()
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,
text_prefix=f"<|startoftranscript|><|{self.language}|><|{self.task}|><|notimestamps|>"
)
if ServeClientTensorRT.SINGLE_MODEL:
ServeClientTensorRT.SINGLE_MODEL_LOCK.release()
if last_segment:
self.handle_transcription_output(last_segment, duration)
def update_timestamp_offset(self, last_segment, duration):
"""
Update timestamp offset and transcript.
Args:
last_segment (str): Last transcribed audio from the whisper model.
duration (float): Duration of the last audio chunk.
"""
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 + " "})
with self.lock:
self.timestamp_offset += duration
def speech_to_text(self):
"""
Process an audio stream in an infinite loop, continuously transcribing the speech.
This method continuously receives audio frames, performs real-time transcription, and sends
transcribed segments to the client via a WebSocket connection.
If the client's language is not detected, it waits for 30 seconds of audio input to make a language prediction.
It utilizes the Whisper ASR model to transcribe the audio, continuously processing and streaming results. Segments
are sent to the client in real-time, and a history of segments is maintained to provide context.Pauses in speech
(no output from Whisper) are handled by showing the previous output for a set duration. A blank segment is added if
there is no speech for a specified duration to indicate a pause.
Raises:
Exception: If there is an issue with audio processing or WebSocket communication.
"""
while True:
if self.exit:
logging.info("Exiting speech to text thread")
break
if self.frames_np is None:
time.sleep(0.02) # wait for any audio to arrive
continue
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}")
self.transcribe_audio(input_sample)
except Exception as e:
logging.error(f"[ERROR]: {e}")
whisper_live/server.py
+31 -735
View File
@@ -7,16 +7,11 @@ import logging
from enum import Enum
from typing import List, Optional
import torch
import numpy as np
from websockets.sync.server import serve
from websockets.exceptions import ConnectionClosed
from whisper_live.vad import VoiceActivityDetector
from whisper_live.transcriber import WhisperModel
try:
from whisper_live.transcriber_tensorrt import WhisperTRTLLM
except Exception:
pass
from whisper_live.backend.base import ServeClientBase
logging.basicConfig(level=logging.INFO)
@@ -127,6 +122,7 @@ class ClientManager:
class BackendType(Enum):
FASTER_WHISPER = "faster_whisper"
TENSORRT = "tensorrt"
OPENVINO = "openvino"
@staticmethod
def valid_types() -> List[str]:
@@ -141,6 +137,9 @@ class BackendType(Enum):
def is_tensorrt(self) -> bool:
return self == BackendType.TENSORRT
def is_openvino(self) -> bool:
return self == BackendType.OPENVINO
class TranscriptionServer:
@@ -160,6 +159,7 @@ class TranscriptionServer:
if self.backend.is_tensorrt():
try:
from whisper_live.backend.trt_backend import ServeClientTensorRT
client = ServeClientTensorRT(
websocket,
multilingual=trt_multilingual,
@@ -180,9 +180,33 @@ class TranscriptionServer:
"Reverting to available backend: 'faster_whisper'"
}))
self.backend = BackendType.FASTER_WHISPER
if self.backend.is_openvino():
try:
from whisper_live.backend.openvino_backend import ServeClientOpenVINO
client = ServeClientOpenVINO(
websocket,
language=options["language"],
task=options["task"],
client_uid=options["uid"],
model=options["model"],
single_model=self.single_model,
)
logging.info("Running OpenVINO backend.")
except Exception as e:
logging.error(f"OpenVINO not supported: {e}")
self.backend = BackendType.FASTER_WHISPER
self.client_uid = options["uid"]
websocket.send(json.dumps({
"uid": self.client_uid,
"status": "WARNING",
"message": "OpenVINO not supported on Server yet. "
"Reverting to available backend: 'faster_whisper'"
}))
try:
if self.backend.is_faster_whisper():
from whisper_live.backend.faster_whisper_backend import ServeClientFasterWhisper
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
@@ -200,6 +224,7 @@ class TranscriptionServer:
logging.info("Running faster_whisper backend.")
except Exception as e:
logging.error(e)
return
if client is None:
@@ -403,732 +428,3 @@ class TranscriptionServer:
if self.client_manager.get_client(websocket):
self.client_manager.remove_client(websocket)
class ServeClientBase(object):
RATE = 16000
SERVER_READY = "SERVER_READY"
DISCONNECT = "DISCONNECT"
def __init__(self, client_uid, websocket):
self.client_uid = client_uid
self.websocket = websocket
self.frames = b""
self.timestamp_offset = 0.0
self.frames_np = None
self.frames_offset = 0.0
self.text = []
self.current_out = ''
self.prev_out = ''
self.t_start = None
self.exit = False
self.same_output_count = 0
self.show_prev_out_thresh = 5 # if pause(no output from whisper) show previous output for 5 seconds
self.add_pause_thresh = 3 # add a blank to segment list as a pause(no speech) for 3 seconds
self.transcript = []
self.send_last_n_segments = 10
# text formatting
self.pick_previous_segments = 2
# 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.
This method is responsible for maintaining the audio stream buffer, allowing the continuous addition
of audio frames as they are received. It also ensures that the buffer does not exceed a specified size
to prevent excessive memory usage.
If the buffer size exceeds a threshold (45 seconds of audio data), it discards the oldest 30 seconds
of audio data to maintain a reasonable buffer size. If the buffer is empty, it initializes it with the provided
audio frame. The audio stream buffer is used for real-time processing of audio data for transcription.
Args:
frame_np (numpy.ndarray): The audio frame data as a NumPy array.
"""
self.lock.acquire()
if self.frames_np is not None and self.frames_np.shape[0] > 45*self.RATE:
self.frames_offset += 30.0
self.frames_np = self.frames_np[int(30*self.RATE):]
# check timestamp offset(should be >= self.frame_offset)
# this basically means that there is no speech as timestamp offset hasnt updated
# and is less than frame_offset
if self.timestamp_offset < self.frames_offset:
self.timestamp_offset = self.frames_offset
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 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
"""
with self.lock:
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):
"""
Retrieves the next chunk of audio data for processing based on the current offsets.
Calculates which part of the audio data should be processed next, based on
the difference between the current timestamp offset and the frame's offset, scaled by
the audio sample rate (RATE). It then returns this chunk of audio data along with its
duration in seconds.
Returns:
tuple: A tuple containing:
- input_bytes (np.ndarray): The next chunk of audio data to be processed.
- duration (float): The duration of the audio chunk in seconds.
"""
with self.lock:
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):
"""
Prepares the segments of transcribed text to be sent to the client.
This method compiles the recent segments of transcribed text, ensuring that only the
specified number of the most recent segments are included. It also appends the most
recent segment of text if provided (which is considered incomplete because of the possibility
of the last word being truncated in the audio chunk).
Args:
last_segment (str, optional): The most recent segment of transcribed text to be added
to the list of segments. Defaults to None.
Returns:
list: A list of transcribed text 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):
"""
Calculates the duration of the provided audio chunk.
Args:
input_bytes (numpy.ndarray): The audio chunk for which to calculate the duration.
Returns:
float: The duration of the audio chunk in seconds.
"""
return input_bytes.shape[0] / self.RATE
def send_transcription_to_client(self, segments):
"""
Sends the specified transcription segments to the client over the websocket connection.
This method formats the transcription segments into a JSON object and attempts to send
this object to the client. If an error occurs during the send operation, it logs the error.
Returns:
segments (list): A list of transcription segments to be sent 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.
This method sends a disconnect message to the client via the WebSocket connection to notify them
that the transcription service is disconnecting gracefully.
"""
self.websocket.send(json.dumps({
"uid": self.client_uid,
"message": self.DISCONNECT
}))
def cleanup(self):
"""
Perform cleanup tasks before exiting the transcription service.
This method performs necessary cleanup tasks, including stopping the transcription thread, marking
the exit flag to indicate the transcription thread should exit gracefully, and destroying resources
associated with the transcription process.
"""
logging.info("Cleaning up.")
self.exit = True
class ServeClientTensorRT(ServeClientBase):
SINGLE_MODEL = None
SINGLE_MODEL_LOCK = threading.Lock()
def __init__(self, websocket, task="transcribe", multilingual=False, language=None, client_uid=None, model=None, single_model=False):
"""
Initialize a ServeClient instance.
The Whisper model is initialized based on the client's language and device availability.
The transcription thread is started upon initialization. A "SERVER_READY" message is sent
to the client to indicate that the server is ready.
Args:
websocket (WebSocket): The WebSocket connection for the client.
task (str, optional): The task type, e.g., "transcribe." Defaults to "transcribe".
device (str, optional): The device type for Whisper, "cuda" or "cpu". Defaults to None.
multilingual (bool, optional): Whether the client supports multilingual transcription. Defaults to False.
language (str, optional): The language for transcription. Defaults to None.
client_uid (str, optional): A unique identifier for the client. Defaults to None.
single_model (bool, optional): Whether to instantiate a new model for each client connection. Defaults to False.
"""
super().__init__(client_uid, websocket)
self.language = language if multilingual else "en"
self.task = task
self.eos = False
if single_model:
if ServeClientTensorRT.SINGLE_MODEL is None:
self.create_model(model, multilingual)
ServeClientTensorRT.SINGLE_MODEL = self.transcriber
else:
self.transcriber = ServeClientTensorRT.SINGLE_MODEL
else:
self.create_model(model, multilingual)
# threading
self.trans_thread = threading.Thread(target=self.speech_to_text)
self.trans_thread.start()
self.websocket.send(json.dumps({
"uid": self.client_uid,
"message": self.SERVER_READY,
"backend": "tensorrt"
}))
def create_model(self, model, multilingual, warmup=True):
"""
Instantiates a new model, sets it as the transcriber and does warmup if desired.
"""
self.transcriber = WhisperTRTLLM(
model,
assets_dir="assets",
device="cuda",
is_multilingual=multilingual,
language=self.language,
task=self.task
)
if warmup:
self.warmup()
def warmup(self, warmup_steps=10):
"""
Warmup TensorRT since first few inferences are slow.
Args:
warmup_steps (int): Number of steps to warm up the model for.
"""
logging.info("[INFO:] Warming up TensorRT engine..")
mel, _ = self.transcriber.log_mel_spectrogram("assets/jfk.flac")
for i in range(warmup_steps):
self.transcriber.transcribe(mel)
def set_eos(self, eos):
"""
Sets the End of Speech (EOS) flag.
Args:
eos (bool): The value to set for the EOS flag.
"""
self.lock.acquire()
self.eos = eos
self.lock.release()
def handle_transcription_output(self, last_segment, duration):
"""
Handle the transcription output, updating the transcript and sending data to the client.
Args:
last_segment (str): The last segment from the whisper output which is considered to be incomplete because
of the possibility of word being truncated.
duration (float): Duration of the transcribed audio chunk.
"""
segments = self.prepare_segments({"text": last_segment})
self.send_transcription_to_client(segments)
if self.eos:
self.update_timestamp_offset(last_segment, duration)
def transcribe_audio(self, input_bytes):
"""
Transcribe the audio chunk and send the results to the client.
Args:
input_bytes (np.array): The audio chunk to transcribe.
"""
if ServeClientTensorRT.SINGLE_MODEL:
ServeClientTensorRT.SINGLE_MODEL_LOCK.acquire()
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,
text_prefix=f"<|startoftranscript|><|{self.language}|><|{self.task}|><|notimestamps|>"
)
if ServeClientTensorRT.SINGLE_MODEL:
ServeClientTensorRT.SINGLE_MODEL_LOCK.release()
if last_segment:
self.handle_transcription_output(last_segment, duration)
def update_timestamp_offset(self, last_segment, duration):
"""
Update timestamp offset and transcript.
Args:
last_segment (str): Last transcribed audio from the whisper model.
duration (float): Duration of the last audio chunk.
"""
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 + " "})
with self.lock:
self.timestamp_offset += duration
def speech_to_text(self):
"""
Process an audio stream in an infinite loop, continuously transcribing the speech.
This method continuously receives audio frames, performs real-time transcription, and sends
transcribed segments to the client via a WebSocket connection.
If the client's language is not detected, it waits for 30 seconds of audio input to make a language prediction.
It utilizes the Whisper ASR model to transcribe the audio, continuously processing and streaming results. Segments
are sent to the client in real-time, and a history of segments is maintained to provide context.Pauses in speech
(no output from Whisper) are handled by showing the previous output for a set duration. A blank segment is added if
there is no speech for a specified duration to indicate a pause.
Raises:
Exception: If there is an issue with audio processing or WebSocket communication.
"""
while True:
if self.exit:
logging.info("Exiting speech to text thread")
break
if self.frames_np is None:
time.sleep(0.02) # wait for any audio to arrive
continue
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}")
self.transcribe_audio(input_sample)
except Exception as e:
logging.error(f"[ERROR]: {e}")
class ServeClientFasterWhisper(ServeClientBase):
SINGLE_MODEL = None
SINGLE_MODEL_LOCK = threading.Lock()
def __init__(self, websocket, task="transcribe", device=None, language=None, client_uid=None, model="small.en",
initial_prompt=None, vad_parameters=None, use_vad=True, single_model=False):
"""
Initialize a ServeClient instance.
The Whisper model is initialized based on the client's language and device availability.
The transcription thread is started upon initialization. A "SERVER_READY" message is sent
to the client to indicate that the server is ready.
Args:
websocket (WebSocket): The WebSocket connection for the client.
task (str, optional): The task type, e.g., "transcribe." Defaults to "transcribe".
device (str, optional): The device type for Whisper, "cuda" or "cpu". Defaults to None.
language (str, optional): The language for transcription. Defaults to None.
client_uid (str, optional): A unique identifier for the client. Defaults to None.
model (str, optional): The whisper model size. Defaults to 'small.en'
initial_prompt (str, optional): Prompt for whisper inference. Defaults to None.
single_model (bool, optional): Whether to instantiate a new model for each client connection. Defaults to False.
"""
super().__init__(client_uid, websocket)
self.model_sizes = [
"tiny", "tiny.en", "base", "base.en", "small", "small.en",
"medium", "medium.en", "large-v2", "large-v3", "distil-small.en",
"distil-medium.en", "distil-large-v2", "distil-large-v3",
"large-v3-turbo", "turbo"
]
self.model_size_or_path = model
self.language = "en" if self.model_size_or_path.endswith("en") else language
self.task = task
self.initial_prompt = initial_prompt
self.vad_parameters = vad_parameters or {"onset": 0.5}
self.no_speech_thresh = 0.45
self.same_output_threshold = 10
self.end_time_for_same_output = None
device = "cuda" if torch.cuda.is_available() else "cpu"
if device == "cuda":
major, _ = torch.cuda.get_device_capability(device)
self.compute_type = "float16" if major >= 7 else "float32"
else:
self.compute_type = "int8"
if self.model_size_or_path is None:
return
logging.info(f"Using Device={device} with precision {self.compute_type}")
try:
if single_model:
if ServeClientFasterWhisper.SINGLE_MODEL is None:
self.create_model(device)
ServeClientFasterWhisper.SINGLE_MODEL = self.transcriber
else:
self.transcriber = ServeClientFasterWhisper.SINGLE_MODEL
else:
self.create_model(device)
except Exception as e:
logging.error(f"Failed to load model: {e}")
self.websocket.send(json.dumps({
"uid": self.client_uid,
"status": "ERROR",
"message": f"Failed to load model: {str(self.model_size_or_path)}"
}))
self.websocket.close()
return
self.use_vad = use_vad
# threading
self.trans_thread = threading.Thread(target=self.speech_to_text)
self.trans_thread.start()
self.websocket.send(
json.dumps(
{
"uid": self.client_uid,
"message": self.SERVER_READY,
"backend": "faster_whisper"
}
)
)
def create_model(self, device):
"""
Instantiates a new model, sets it as the transcriber.
"""
self.transcriber = WhisperModel(
self.model_size_or_path,
device=device,
compute_type=self.compute_type,
local_files_only=False,
)
def check_valid_model(self, model_size):
"""
Check if it's a valid whisper model size.
Args:
model_size (str): The name of the model size to check.
Returns:
str: The model size if valid, None otherwise.
"""
if model_size not in self.model_sizes:
self.websocket.send(
json.dumps(
{
"uid": self.client_uid,
"status": "ERROR",
"message": f"Invalid model size {model_size}. Available choices: {self.model_sizes}"
}
)
)
return None
return model_size
def set_language(self, info):
"""
Updates the language attribute based on the detected language information.
Args:
info (object): An object containing the detected language and its probability. This object
must have at least two attributes: `language`, a string indicating the detected
language, and `language_probability`, a float representing the confidence level
of the language detection.
"""
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):
"""
Transcribes the provided audio sample using the configured transcriber instance.
If the language has not been set, it updates the session's language based on the transcription
information.
Args:
input_sample (np.array): The audio chunk to be transcribed. This should be a NumPy
array representing the audio data.
Returns:
The transcription result from the transcriber. The exact format of this result
depends on the implementation of the `transcriber.transcribe` method but typically
includes the transcribed text.
"""
if ServeClientFasterWhisper.SINGLE_MODEL:
ServeClientFasterWhisper.SINGLE_MODEL_LOCK.acquire()
result, info = self.transcriber.transcribe(
input_sample,
initial_prompt=self.initial_prompt,
language=self.language,
task=self.task,
vad_filter=self.use_vad,
vad_parameters=self.vad_parameters if self.use_vad else None)
if ServeClientFasterWhisper.SINGLE_MODEL:
ServeClientFasterWhisper.SINGLE_MODEL_LOCK.release()
if self.language is None and info is not None:
self.set_language(info)
return result
def get_previous_output(self):
"""
Retrieves previously generated transcription outputs if no new transcription is available
from the current audio chunks.
Checks the time since the last transcription output and, if it is within a specified
threshold, returns the most recent segments of transcribed text. It also manages
adding a pause (blank segment) to indicate a significant gap in speech based on a defined
threshold.
Returns:
segments (list): A list of transcription segments. This may include the most recent
transcribed text segments or a blank segment to indicate a pause
in speech.
"""
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 handle_transcription_output(self, result, duration):
"""
Handle the transcription output, updating the transcript and sending data to the client.
Args:
result (str): The result from whisper inference i.e. the list of segments.
duration (float): Duration of the transcribed audio chunk.
"""
segments = []
if len(result):
self.t_start = None
last_segment = self.update_segments(result, duration)
segments = self.prepare_segments(last_segment)
else:
# show previous output if there is pause i.e. no output from whisper
segments = self.get_previous_output()
if len(segments):
self.send_transcription_to_client(segments)
def speech_to_text(self):
"""
Process an audio stream in an infinite loop, continuously transcribing the speech.
This method continuously receives audio frames, performs real-time transcription, and sends
transcribed segments to the client via a WebSocket connection.
If the client's language is not detected, it waits for 30 seconds of audio input to make a language prediction.
It utilizes the Whisper ASR model to transcribe the audio, continuously processing and streaming results. Segments
are sent to the client in real-time, and a history of segments is maintained to provide context.Pauses in speech
(no output from Whisper) are handled by showing the previous output for a set duration. A blank segment is added if
there is no speech for a specified duration to indicate a pause.
Raises:
Exception: If there is an issue with audio processing or WebSocket communication.
"""
while True:
if self.exit:
logging.info("Exiting speech to text thread")
break
if self.frames_np is None:
continue
self.clip_audio_if_no_valid_segment()
input_bytes, duration = self.get_audio_chunk_for_processing()
if duration < 1.0:
time.sleep(0.1) # wait for audio chunks to arrive
continue
try:
input_sample = input_bytes.copy()
result = self.transcribe_audio(input_sample)
if result is None or self.language is None:
self.timestamp_offset += duration
time.sleep(0.25) # wait for voice activity, result is None when no voice activity
continue
self.handle_transcription_output(result, duration)
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, completed=False):
"""
Formats a transcription segment with precise start and end times alongside the transcribed text.
Args:
start (float): The start time of the transcription segment in seconds.
end (float): The end time of the transcription segment in seconds.
text (str): The transcribed text corresponding to the segment.
Returns:
dict: A dictionary representing the formatted transcription segment, including
'start' and 'end' times as strings with three decimal places and the 'text'
of the transcription.
"""
return {
'start': "{:.3f}".format(start),
'end': "{:.3f}".format(end),
'text': text,
'completed': completed
}
def update_segments(self, segments, duration):
"""
Processes the segments from whisper. Appends all the segments to the list
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
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
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.
"""
offset = None
self.current_out = ''
last_segment = None
# process complete segments
if len(segments) > 1 and segments[-1].no_speech_prob <= self.no_speech_thresh:
for i, s in enumerate(segments[:-1]):
text_ = s.text
self.text.append(text_)
with self.lock:
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
self.transcript.append(self.format_segment(start, end, text_, completed=True))
offset = min(duration, s.end)
# only process the last segment if it satisfies the no_speech_thresh
if segments[-1].no_speech_prob <= self.no_speech_thresh:
self.current_out += segments[-1].text
with self.lock:
last_segment = self.format_segment(
self.timestamp_offset + segments[-1].start,
self.timestamp_offset + min(duration, segments[-1].end),
self.current_out,
completed=False
)
if self.current_out.strip() == self.prev_out.strip() and self.current_out != '':
self.same_output_count += 1
# if we remove the audio because of same output on the nth reptition we might remove the
# audio thats not yet transcribed so, capturing the time when it was repeated for the first time
if self.end_time_for_same_output is None:
self.end_time_for_same_output = segments[-1].end
time.sleep(0.1) # wait for some voice activity just in case there is an unitended pause from the speaker for better punctuations.
else:
self.same_output_count = 0
self.end_time_for_same_output = None
# if same incomplete segment is seen multiple times then update the offset
# and append the segment to the list
if self.same_output_count > self.same_output_threshold:
if not len(self.text) or self.text[-1].strip().lower() != self.current_out.strip().lower():
self.text.append(self.current_out)
with self.lock:
self.transcript.append(self.format_segment(
self.timestamp_offset,
self.timestamp_offset + min(duration, self.end_time_for_same_output),
self.current_out,
completed=True
))
self.current_out = ''
offset = min(duration, self.end_time_for_same_output)
self.same_output_count = 0
last_segment = None
self.end_time_for_same_output = None
else:
self.prev_out = self.current_out
# update offset
if offset is not None:
with self.lock:
self.timestamp_offset += offset
return last_segment
whisper_live/transcriber/__init__.py
View File
whisper_live/tensorrt_utils.py → whisper_live/transcriber/tensorrt_utils.py
View File
whisper_live/transcriber.py → whisper_live/transcriber/transcriber_faster_whisper.py
View File
whisper_live/transcriber/transcriber_openvino.py
+23
View File
@@ -0,0 +1,23 @@
import librosa
import os
import openvino_genai as ov_genai
import huggingface_hub as hf_hub
class WhisperOpenVINO(object):
def __init__(self, model_id="OpenVINO/whisper-tiny-fp16-ov", device="CPU", language="en", task="transcribe"):
model_path = model_id.split('/')[-1]
cache_dir = os.path.join(os.path.expanduser("~"), ".cache", "openvino_whisper_models")
os.makedirs(cache_dir, exist_ok=True)
model_path = os.path.join(cache_dir, model_path)
if not os.path.exists(model_path):
hf_hub.snapshot_download(model_id, local_dir=model_path)
self.model = ov_genai.WhisperPipeline(str(model_path), device=device)
self.language = language
self.task = task
def transcribe(self, input_audio):
outputs = self.model.generate(input_audio, return_timestamps=True, language=self.language, task=self.task)
outputs = [seg for seg in outputs.chunks]
return outputs
whisper_live/transcriber_tensorrt.py → whisper_live/transcriber/transcriber_tensorrt.py
+6 -1
View File
@@ -9,7 +9,12 @@ 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, load_audio_wav_format, pad_or_trim, load_audio)
from whisper_live.transcriber.tensorrt_utils import (
mel_filters,
load_audio_wav_format,
pad_or_trim,
load_audio
)
import tensorrt_llm
import tensorrt_llm.logger as logger