diff --git a/requirements.txt b/requirements.txt
index b590313..5d14006 100755
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,6 @@
git+https://github.com/openai/whisper.git
+git+https://github.com/m-bain/whisperx.git
+
more-itertools
ffmpeg-python
gradio
diff --git a/src/utils.py b/src/utils.py
index eb95d19..cfe5f97 100755
--- a/src/utils.py
+++ b/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()
\ No newline at end of file
+ do_gc()
\ No newline at end of file
diff --git a/src/webui.py b/src/webui.py
index f9b06ca..b04d57e 100755
--- a/src/webui.py
+++ b/src/webui.py
@@ -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",
)
diff --git a/update.sh b/update.sh
index ccdf720..728a14a 100755
--- a/update.sh
+++ b/update.sh
@@ -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
\ No newline at end of file