added api_name for generation, added whisperx backend, relocated use whispercpp option to whisper backend list
This commit is contained in:
parent
788a957f79
commit
0e3bbc55f8
|
|
@ -1,4 +1,6 @@
|
|||
git+https://github.com/openai/whisper.git
|
||||
git+https://github.com/m-bain/whisperx.git
|
||||
|
||||
more-itertools
|
||||
ffmpeg-python
|
||||
gradio
|
||||
|
|
|
|||
71
src/utils.py
71
src/utils.py
|
|
@ -37,8 +37,11 @@ from tortoise.utils.text import split_and_recombine_text
|
|||
from tortoise.utils.device import get_device_name, set_device_name
|
||||
|
||||
MODELS['dvae.pth'] = "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/3704aea61678e7e468a06d8eea121dba368a798e/.models/dvae.pth"
|
||||
WHISPER_MODELS = ["tiny", "base", "small", "medium", "large"]
|
||||
|
||||
WHISPER_MODELS = ["tiny", "base", "small", "medium", "large", "large-v2"]
|
||||
WHISPER_SPECIALIZED_MODELS = ["tiny.en", "base.en", "small.en", "medium.en"]
|
||||
WHISPER_BACKENDS = ["openai/whisper", "lightmare/whispercpp", "m-bain/whisperx"]
|
||||
|
||||
EPOCH_SCHEDULE = [ 9, 18, 25, 33 ]
|
||||
|
||||
args = None
|
||||
|
|
@ -1001,28 +1004,38 @@ def whisper_transcribe( file, language=None ):
|
|||
if not whisper_model:
|
||||
load_whisper_model(language=language)
|
||||
|
||||
if not args.whisper_cpp:
|
||||
if args.whisper_backend == "openai/whisper":
|
||||
if not language:
|
||||
language = None
|
||||
|
||||
return whisper_model.transcribe(file, language=language)
|
||||
|
||||
res = whisper_model.transcribe(file)
|
||||
segments = whisper_model.extract_text_and_timestamps( res )
|
||||
elif args.whisper_backend == "lightmare/whispercpp":
|
||||
res = whisper_model.transcribe(file)
|
||||
segments = whisper_model.extract_text_and_timestamps( res )
|
||||
|
||||
result = {
|
||||
'segments': []
|
||||
}
|
||||
for segment in segments:
|
||||
reparsed = {
|
||||
'start': segment[0] / 100.0,
|
||||
'end': segment[1] / 100.0,
|
||||
'text': segment[2],
|
||||
result = {
|
||||
'segments': []
|
||||
}
|
||||
result['segments'].append(reparsed)
|
||||
for segment in segments:
|
||||
reparsed = {
|
||||
'start': segment[0] / 100.0,
|
||||
'end': segment[1] / 100.0,
|
||||
'text': segment[2],
|
||||
}
|
||||
result['segments'].append(reparsed)
|
||||
|
||||
return result
|
||||
return result
|
||||
|
||||
# credit to https://git.ecker.tech/yqxtqymn for the busywork of getting this added
|
||||
elif args.whisper_backend == "m-bain/whisperx":
|
||||
import whisperx
|
||||
device = "cuda" if get_device_name() == "cuda" else "cpu"
|
||||
result = whisper_model.transcribe(file)
|
||||
model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
|
||||
result_aligned = whisperx.align(result["segments"], model_a, metadata, file, device)
|
||||
|
||||
return result
|
||||
|
||||
def prepare_dataset( files, outdir, language=None, progress=None ):
|
||||
unload_tts()
|
||||
|
|
@ -1425,8 +1438,8 @@ def setup_args():
|
|||
'output-volume': 1,
|
||||
|
||||
'autoregressive-model': None,
|
||||
'whisper-backend': 'openai/whisper',
|
||||
'whisper-model': "base",
|
||||
'whisper-cpp': False,
|
||||
|
||||
'training-default-halfp': False,
|
||||
'training-default-bnb': True,
|
||||
|
|
@ -1464,8 +1477,8 @@ def setup_args():
|
|||
parser.add_argument("--output-volume", type=float, default=default_arguments['output-volume'], help="Adjusts volume of output")
|
||||
|
||||
parser.add_argument("--autoregressive-model", default=default_arguments['autoregressive-model'], help="Specifies which autoregressive model to use for sampling.")
|
||||
parser.add_argument("--whisper-backend", default=default_arguments['whisper-backend'], action='store_true', help="Picks which whisper backend to use (openai/whisper, lightmare/whispercpp, m-bain/whisperx)")
|
||||
parser.add_argument("--whisper-model", default=default_arguments['whisper-model'], help="Specifies which whisper model to use for transcription.")
|
||||
parser.add_argument("--whisper-cpp", default=default_arguments['whisper-cpp'], action='store_true', help="Leverages lightmare/whispercpp for transcription")
|
||||
|
||||
parser.add_argument("--training-default-halfp", action='store_true', default=default_arguments['training-default-halfp'], help="Training default: halfp")
|
||||
parser.add_argument("--training-default-bnb", action='store_true', default=default_arguments['training-default-bnb'], help="Training default: bnb")
|
||||
|
|
@ -1499,7 +1512,7 @@ def setup_args():
|
|||
|
||||
return args
|
||||
|
||||
def update_args( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, defer_tts_load, prune_nonfinal_outputs, use_bigvgan_vocoder, device_override, sample_batch_size, concurrency_count, autocalculate_voice_chunk_duration_size, output_volume, autoregressive_model, whisper_model, whisper_cpp, training_default_halfp, training_default_bnb ):
|
||||
def update_args( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, defer_tts_load, prune_nonfinal_outputs, use_bigvgan_vocoder, device_override, sample_batch_size, concurrency_count, autocalculate_voice_chunk_duration_size, output_volume, autoregressive_model, whisper_backend, whisper_model, training_default_halfp, training_default_bnb ):
|
||||
global args
|
||||
|
||||
args.listen = listen
|
||||
|
|
@ -1523,8 +1536,8 @@ def update_args( listen, share, check_for_updates, models_from_local_only, low_v
|
|||
args.output_volume = output_volume
|
||||
|
||||
args.autoregressive_model = autoregressive_model
|
||||
args.whisper_backend = whisper_backend
|
||||
args.whisper_model = whisper_model
|
||||
args.whisper_cpp = whisper_cpp
|
||||
|
||||
args.training_default_halfp = training_default_halfp
|
||||
args.training_default_bnb = training_default_bnb
|
||||
|
|
@ -1555,8 +1568,8 @@ def save_args_settings():
|
|||
'output-volume': args.output_volume,
|
||||
|
||||
'autoregressive-model': args.autoregressive_model,
|
||||
'whisper-backend': args.whisper_backend,
|
||||
'whisper-model': args.whisper_model,
|
||||
'whisper-cpp': args.whisper_cpp,
|
||||
|
||||
'training-default-halfp': args.training_default_halfp,
|
||||
'training-default-bnb': args.training_default_bnb,
|
||||
|
|
@ -1761,6 +1774,12 @@ def unload_voicefixer():
|
|||
|
||||
def load_whisper_model(language=None, model_name=None, progress=None):
|
||||
global whisper_model
|
||||
|
||||
if args.whisper_backend not in WHISPER_BACKENDS:
|
||||
raise Exception(f"unavailable backend: {args.whisper_backend}")
|
||||
|
||||
if args.whisper_backend != "m-bain/whisperx" and model_name == "large-v2":
|
||||
raise Exception("large-v2 is only available for m-bain/whisperx backend")
|
||||
|
||||
if not model_name:
|
||||
model_name = args.whisper_model
|
||||
|
|
@ -1774,16 +1793,20 @@ def load_whisper_model(language=None, model_name=None, progress=None):
|
|||
|
||||
notify_progress(f"Loading Whisper model: {model_name}", progress)
|
||||
|
||||
if args.whisper_cpp:
|
||||
if args.whisper_backend == "openai/whisper":
|
||||
import whisper
|
||||
whisper_model = whisper.load_model(model_name)
|
||||
elif args.whisper_backend == "lightmare/whispercpp":
|
||||
from whispercpp import Whisper
|
||||
if not language:
|
||||
language = 'auto'
|
||||
|
||||
b_lang = language.encode('ascii')
|
||||
whisper_model = Whisper(model_name, models_dir='./models/', language=b_lang)
|
||||
else:
|
||||
import whisper
|
||||
whisper_model = whisper.load_model(model_name)
|
||||
elif args.whisper_backend == "m-bain/whisperx":
|
||||
import whisperx
|
||||
device = "cuda" if get_device_name() == "cuda" else "cpu"
|
||||
whisper_model = whisperx.load_model(model_name, device)
|
||||
|
||||
print("Loaded Whisper model")
|
||||
|
||||
|
|
@ -1795,4 +1818,4 @@ def unload_whisper():
|
|||
whisper_model = None
|
||||
print("Unloaded Whisper")
|
||||
|
||||
do_gc()
|
||||
do_gc()
|
||||
|
|
@ -589,10 +589,10 @@ def setup_gradio():
|
|||
|
||||
autoregressive_model_dropdown = gr.Dropdown(choices=autoregressive_models, label="Autoregressive Model", value=args.autoregressive_model if args.autoregressive_model else autoregressive_models[0])
|
||||
|
||||
whisper_backend = gr.Dropdown(WHISPER_BACKENDS, label="Whisper Backends", value=args.whisper_backend)
|
||||
whisper_model_dropdown = gr.Dropdown(WHISPER_MODELS, label="Whisper Model", value=args.whisper_model)
|
||||
use_whisper_cpp = gr.Checkbox(label="Use Whisper.cpp", value=args.whisper_cpp)
|
||||
|
||||
exec_inputs = exec_inputs + [ autoregressive_model_dropdown, whisper_model_dropdown, use_whisper_cpp, training_halfp, training_bnb ]
|
||||
exec_inputs = exec_inputs + [ autoregressive_model_dropdown, whisper_backend, whisper_model_dropdown, training_halfp, training_bnb ]
|
||||
|
||||
with gr.Row():
|
||||
autoregressive_models_update_button = gr.Button(value="Refresh Model List")
|
||||
|
|
@ -732,6 +732,7 @@ def setup_gradio():
|
|||
submit_event = submit.click(run_generation,
|
||||
inputs=input_settings,
|
||||
outputs=[output_audio, source_sample, candidates_list, generation_results],
|
||||
api_name="generate",
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -3,5 +3,6 @@ git pull
|
|||
git submodule update --remote
|
||||
|
||||
if python -m pip show whispercpp &>/dev/null; then python -m pip install -U git+https://git.ecker.tech/lightmare/whispercpp.py; fi
|
||||
if python -m pip show whisperx &>/dev/null; then python -m pip install -U git+https://github.com/m-bain/whisperx.git; fi
|
||||
|
||||
deactivate
|
||||
Loading…
Reference in New Issue
Block a user