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| #!/usr/bin/python3 | |
| import os | |
| import torch | |
| from audiosr import super_resolution, build_model, save_wave, get_time, read_list | |
| from pyharp import ModelCard, build_endpoint | |
| from audiotools import AudioSignal | |
| import scipy | |
| import torch | |
| import gradio as gr | |
| card = ModelCard( | |
| name='Versatile Audio Super Resolution', | |
| description='Upsample audio and predict upper spectrum.', | |
| author='Team Audio', | |
| tags=['AudioSR', 'Diffusion', 'Super Resolution', 'Upsampling', 'Sample Rate Conversion'] | |
| ) | |
| os.environ["TOKENIZERS_PARALLELISM"] = "true" | |
| torch.set_float32_matmul_precision("high") | |
| latent_t_per_second=12.8 # not sure about this?? | |
| audiosr = build_model(model_name="basic", device="auto") | |
| def process_fn(input_audio_path, seed, guidance_scale, num_inference_steps): | |
| """ | |
| This function defines the audio processing steps | |
| Args: | |
| input_audio_path (str): the audio filepath to be processed. | |
| <YOUR_KWARGS>: additional keyword arguments necessary for processing. | |
| NOTE: These should correspond to and match order of UI elements defined below. | |
| Returns: | |
| output_audio_path (str): the filepath of the processed audio. | |
| """ | |
| sig = AudioSignal(input_audio_path, sample_rate=16000) | |
| outfile = "./output.wav" | |
| audio_concat = None | |
| total_length = sig.duration | |
| print(f"Total length: {total_length}") | |
| num_segs = int(total_length / 5.12) # 5.12 second segments | |
| print(f"Number of segments: {num_segs}") | |
| remainder = total_length % 5.12 # duration of last segment | |
| print(f"Remainder: {remainder}") | |
| for audio_segment in range(num_segs): | |
| print(f"Processing segment {audio_segment} of {num_segs}") | |
| start = audio_segment * 5.12 | |
| if audio_segment == num_segs - 1: | |
| end = start + remainder | |
| else: | |
| end = start + 5.12 | |
| # get segment of audio from original file | |
| sig_seg = sig[..., int(start*sig.sample_rate):int(end*sig.sample_rate)] # int accounts for float end time on last seg | |
| print(f"Segment length: {sig_seg.duration}") | |
| print(f"Segment start: {start}") | |
| print(f"Segment end: {end}") | |
| print(f"Segment start sample: {int(start*sig.sample_rate)}") | |
| print(f"Segment end sample: {int(end*sig.sample_rate)}") | |
| sig_seg.write(f"temp_{audio_segment}.wav") | |
| audio = super_resolution( | |
| audiosr, | |
| f"temp_{audio_segment}.wav", | |
| seed=seed, | |
| guidance_scale=guidance_scale, | |
| ddim_steps=num_inference_steps, | |
| latent_t_per_second=latent_t_per_second | |
| ) | |
| #save_wave(waveform, output_dir, name=name, samplerate=sig.sample_rate) | |
| if audio_concat is None: | |
| audio_concat = audio | |
| #audio_concat = audio[0] | |
| else: | |
| audio_concat += audio | |
| scipy.io.wavfile.write(outfile, rate=48000, data=audio_concat) | |
| return outfile | |
| # Build the endpoint | |
| with gr.Blocks() as webapp: | |
| # Define your Gradio interface | |
| inputs = [ | |
| gr.Audio( | |
| label="Audio Input", | |
| type="filepath" | |
| ), | |
| gr.Slider( | |
| label="seed", | |
| minimum="0", | |
| maximum="65535", | |
| value="0", | |
| step="1" | |
| ), | |
| gr.Slider( | |
| minimum=0, maximum=10, | |
| value=3.5, | |
| label="Guidance Scale" | |
| ), | |
| gr.Slider( | |
| minimum=1, maximum=500, | |
| step=1, value=50, | |
| label="Inference Steps" | |
| ), | |
| ] | |
| # make an output audio widget | |
| output = gr.Audio(label="Audio Output", type="filepath") | |
| # Build the endpoint | |
| ctrls_data, ctrls_button, process_button, cancel_button = build_endpoint(inputs, output, process_fn, card) | |
| #webapp.queue() | |
| webapp.launch() |