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import wandb | |
import gradio as gr | |
from datetime import datetime, timedelta | |
import nemo.collections.asr as nemo_asr | |
wandb_api = wandb.Api() | |
all_artifacts_versions = [ | |
version | |
for version in [ | |
collection.versions() | |
for collection in wandb_api.artifact_type( | |
type_name="model", project="nemo-experiments" | |
).collections() | |
] | |
] | |
latest_artifacts = [ | |
artifact | |
for artifact_versions in all_artifacts_versions | |
for artifact in artifact_versions | |
if ( | |
datetime.fromisoformat(artifact.created_at) | |
> datetime.now() - timedelta(days=180) # last 180 days | |
and artifact.state != "DELETED" | |
) | |
] | |
models = {artifact.name: None for artifact in latest_artifacts} | |
def lazy_load_models(models_names): | |
for model_name in models_names: | |
model = models[model_name] | |
if not model: | |
models[model_name] = nemo_asr.models.ASRModel.restore_from( | |
list(filter(lambda x: x.name == model_name, latest_artifacts))[0].file() | |
) | |
models[model_name].eval() | |
def transcribe(audio_mic, audio_file, models_names): | |
lazy_load_models(models_names) | |
# transcribe audio_mic and audio_file separately | |
# because transcribe() fails is path is empty | |
transcription_mic = "\n".join( | |
[ | |
f"{model_name} => {models[model_name].transcribe([audio_mic])[0]}" | |
for model_name in models_names | |
] | |
if audio_mic | |
else "" | |
) | |
transcription_file = "\n".join( | |
[ | |
f"{model_name} => {models[model_name].transcribe([audio_file])[0]}" | |
for model_name in models_names | |
] | |
if audio_file | |
else "" | |
) | |
return transcription_mic, transcription_file | |
model_selection = list(models.keys()) | |
demo = gr.Blocks() | |
with demo: | |
gr.Markdown( | |
""" | |
# ﷽ | |
These are the latest* Tarteel models. | |
Please note that the models are lazy loaded. | |
This means that the first time you use a model, | |
it might take some time to be downloaded and loaded for inference. | |
*: last 180 days since the space was launched. | |
To update the list, restart the space. | |
""" | |
) | |
with gr.Row(): | |
audio_mic = gr.Audio(source="microphone", type="filepath", label="Microphone") | |
audio_file = gr.Audio(source="upload", type="filepath", label="File") | |
with gr.Row(): | |
output_mic = gr.TextArea(label="Microphone Transcription") | |
output_file = gr.TextArea(label="Audio Transcription") | |
models_names = gr.CheckboxGroup(model_selection, label="Select Models to Use") | |
b1 = gr.Button("Transcribe") | |
b1.click( | |
transcribe, | |
inputs=[audio_mic, audio_file, models_names], | |
outputs=[output_mic, output_file], | |
) | |
demo.launch() | |