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()