# WhisperEngine

# Copyright (C) 2024-2026 Collabora Ltd.

#

# This library is free software; you can redistribute it and/or

# modify it under the terms of the GNU Library General Public

# License as published by the Free Software Foundation; either

# version 2 of the License, or (at your option) any later version.

#

# This library is distributed in the hope that it will be useful,

# but WITHOUT ANY WARRANTY; without even the implied warranty of

# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU

# Library General Public License for more details.

#

# You should have received a copy of the GNU Library General Public

# License along with this library; if not, write to the

# Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor,

# Boston, MA 02110-1301, USA.

from .hub_causal_lm import HUB_NAME_SEPARATOR

from .ml_engine import MLEngine

WHISPER_SAMPLE_RATE = 16000

OPENAI_WHISPER_PREFIX = "openai/whisper-"

# faster-whisper takes a size name, the other engines the hub checkpoint

def hub_whisper_name(model_name):

if HUB_NAME_SEPARATOR in model_name:

return model_name

return f"{OPENAI_WHISPER_PREFIX}{model_name}"

class ExportedWhisper:

def __init__(self, model_name):

self.engine = None

self.path = hub_whisper_name(model_name)

def transcribe(self, audio, language, task, beam_size, initial_prompt):

return self.engine.model.transcribe(

audio, language, task, beam_size, initial_prompt

)

class WhisperEngine(MLEngine):

def do_load_model(self, model_name, **kwargs):

from faster_whisper import WhisperModel

if not model_name:

return

compute_type = "float16" if self.device.startswith("cuda") else "int8"

self.logger.info(

f"Loading Whisper model on device: {self.device} with compute_type: {compute_type}"

)

self.model = WhisperModel(

model_name, device=self.device, compute_type=compute_type

)

def transcribe(self, audio, language, task, beam_size, initial_prompt):

segments, _ = self.model.transcribe(

audio,

language=language,

task=task,

beam_size=beam_size,

initial_prompt=initial_prompt,

)

return [segment.text.strip() for segment in segments]