draft of separate audios

app.py

@@ -1,4 +1,57 @@
 import gradio as gr
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+import soundfile as sf
+import numpy as np
+import os
+# import torch
+
+SAMPLE_RATE = 8000
+
+def get_sample_rate(audio_file_path):
+    _, sample_rate = sf.read(audio_file_path, always_2d=True)
+    return sample_rate
+
+def change_sample_rate(input_audio_file_path, output_audio_file_path, sample_rate):
+    # do ffmpeg -i $input_audio_file_path -ar $sample_rate $output_audio_file_path
+    os.system(f'ffmpeg -i {input_audio_file_path} -ar {sample_rate} {output_audio_file_path}')
+
+def audio_is_stereo(audio_file_path):
+    audio, _ = sf.read(audio_file_path, always_2d=True)
+    return audio.shape[1] == 2
+
+def set_mono(input_audio_file_path, output_audio_file_path):
+    os.system(f'ffmpeg -i {input_audio_file_path} -ac 1 {output_audio_file_path}')
+
+os.system('wget https://maximofn.com/wp-content/uploads/2023/10/vocals.wav')
+input = "vocals.wav"
+input_8k = "vocals_8k.wav"
+input_8k_mono = "vocals_8k_mono.wav"
+
+sr = get_sample_rate(input)
+
+if sr != SAMPLE_RATE:
+    change_sample_rate(input, input_8k, SAMPLE_RATE)
+else:
+    input_8k = input
+
+if audio_is_stereo(input_8k):
+    set_mono(input_8k, input_8k_mono)
+else:
+    input_8k_mono = input_8k
+
+# device = 'cuda' if torch.cuda.is_available() else 'cpu'
+device = 'cpu'
+separation = pipeline(Tasks.speech_separation, model='damo/speech_mossformer_separation_temporal_8k', device=device)
+print("Separating...")
+result = separation(input_8k_mono)
+print("Separated!")
+
+print("Saving...")
+for i, signal in enumerate(result['output_pcm_list']):
+    save_file = f'output_spk{i}.wav'
+    sf.write(save_file, np.frombuffer(signal, dtype=np.int16), SAMPLE_RATE)
+print("Saved!")
 
 
 with gr.Blocks() as demo: