up

app.py

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+import gradio as gr
+import numpy as np
+import torch
+import torchaudio
+import sys
+
+
+def convert_to_16_bit_wav(data):
+    # Based on: https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.wavfile.write.html
+    #breakpoint()
+    if data.dtype == np.float32:
+        print(
+            "Audio data is not in 16-bit integer format.",
+            "Trying to convert to 16-bit int format.",
+            file=sys.stderr
+        )
+        data = data / np.abs(data).max()
+        data = data * 32767
+        data = data.astype(np.int16)
+    elif data.dtype == np.int32:
+        print(
+            "Audio data is not in 16-bit integer format.",
+            "Trying to convert to 16-bit int format.",
+            file=sys.stderr
+        )
+        data = data / 65538
+        data = data.astype(np.int16)
+    elif data.dtype == np.int16:
+        pass
+    elif data.dtype == np.uint8:
+        print(
+            "Audio data is not in 16-bit integer format.",
+            "Trying to convert to 16-bit int format.",
+            file=sys.stderr
+        )
+        data = data * 257 - 32768
+        data = data.astype(np.int16)
+    else:
+        raise ValueError("Audio data cannot be converted to " "16-bit int format.")
+    return data
+
+def pcm2float(sig, dtype='float32'):
+    """
+    https://gist.github.com/HudsonHuang/fbdf8e9af7993fe2a91620d3fb86a182
+    """
+    sig = np.asarray(sig)
+    if sig.dtype.kind not in 'iu':
+        raise TypeError("'sig' must be an array of integers")
+    dtype = np.dtype(dtype)
+    if dtype.kind != 'f':
+        raise TypeError("'dtype' must be a floating point type")
+
+    i = np.iinfo(sig.dtype)
+    abs_max = 2 ** (i.bits - 1)
+    offset = i.min + abs_max
+    return (sig.astype(dtype) - offset) / abs_max
+
+
+def float2pcm(sig, dtype='int16'):
+    """
+    https://gist.github.com/HudsonHuang/fbdf8e9af7993fe2a91620d3fb86a182
+    """
+    sig = np.asarray(sig)
+    if sig.dtype.kind != 'f':
+        raise TypeError("'sig' must be a float array")
+    dtype = np.dtype(dtype)
+    if dtype.kind not in 'iu':
+        raise TypeError("'dtype' must be an integer type")
+    i = np.iinfo(dtype)
+    abs_max = 2 ** (i.bits - 1)
+    offset = i.min + abs_max
+    return (sig * abs_max + offset).clip(i.min, i.max).astype(dtype)
+
+
+@torch.no_grad()
+def inference(audio, model_tag):
+  sr, audio = audio
+  audio = convert_to_16_bit_wav(audio)
+  audio = pcm2float(audio)
+  audio = torch.tensor(audio).unsqueeze(0)
+  audio = torchaudio.transforms.Resample(orig_freq=sr,
+                                         new_freq=16000)(audio)
+  print(model_tag, file=sys.stderr)
+  model = torch.hub.load("deep-privacy/SA-toolkit", "anonymization", tag_version=model_tag, trust_repo=True)
+  model.eval()
+  wav_conv = model.convert(audio, target="6081") # hard coded target
+  return 16000, float2pcm(wav_conv.squeeze().cpu().numpy())
+
+
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2308.04455' target='_blank'>PhD thesis: Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques</a> | <a href='https://github.com/deep-privacy/SA-toolkit' target='_blank'>Github Repo</a></p>"
+
+
+def toggle_audio_src(choice):
+    if choice == "mic":
+        return gr.update(source="microphone", value=None, label="Microphone (best with a headset)")
+    else:
+        return gr.update(source="upload", value=None, label="File")
+
+with gr.Blocks() as interface:
+  gr.Markdown(
+            """
+            # SA-toolkit
+            Demo: Speaker speech anonymization toolkit in python 
+            """
+        )
+  with gr.Row():
+    with gr.Column():
+      radio = gr.Radio(["file", "mic"], value="file",
+                       label="Input speech (File or Mic)")
+      audio_input = gr.Audio(source="upload", type="numpy", label="File",
+                        interactive=True, elem_id="melody-input")
+      model_tag = gr.Dropdown(['hifigan_bn_tdnnf_wav2vec2_vq_48_v1',
+                              'hifigan_bn_tdnnf_wav2vec2_100h_aug_v1',
+                              'hifigan_bn_tdnnf_600h_aug_v1',
+                              'hifigan_bn_tdnnf_100h_vq_64_v1',
+                              'hifigan_bn_tdnnf_100h_vq_256_v1',
+                              'hifigan_bn_tdnnf_100h_aug_v1'], type='value',
+                              value='hifigan_bn_tdnnf_wav2vec2_vq_48_v1',
+                             label='Model')
+      with gr.Row():
+        submit = gr.Button("Submit")
+    with gr.Column():
+      audio_output = gr.Audio(label="Output")
+  submit.click(inference, inputs=[audio_input, model_tag],
+                 outputs=[audio_output], batch=False)
+  radio.change(toggle_audio_src, radio, [audio_input], queue=False, show_progress=False)
+  gr.Examples(fn=inference,
+              examples=[['3853-163249-0000.flac']],
+              inputs=[audio_input, "hifigan_bn_tdnnf_wav2vec2_vq_48_v1"],
+              outputs=[audio_output], batch=False)
+
+
+  gr.HTML(article)
+  interface.queue().launch()

requirements.txt

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+torch
+torchaudio
+numpy