Matthijs Hollemans commited on
Commit
caaf71e
1 Parent(s): c66db33

add randomized voice

Browse files
app.py CHANGED
@@ -15,6 +15,7 @@ vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
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  speaker_embeddings = {
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  "BDL": "spkemb/cmu_us_bdl_arctic-wav-arctic_a0009.npy",
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  "CLB": "spkemb/cmu_us_clb_arctic-wav-arctic_a0144.npy",
 
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  "RMS": "spkemb/cmu_us_rms_arctic-wav-arctic_b0353.npy",
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  "SLT": "spkemb/cmu_us_slt_arctic-wav-arctic_a0508.npy",
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  }
@@ -26,7 +27,24 @@ def predict(text, speaker):
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  inputs = processor(text=text, return_tensors="pt")
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- speaker_embedding = np.load(speaker_embeddings[speaker[:3]])
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  speaker_embedding = torch.tensor(speaker_embedding).unsqueeze(0)
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  speech = model.generate_speech(inputs["input_ids"], speaker_embedding, vocoder=vocoder)
@@ -48,6 +66,7 @@ and the <a href="https://huggingface.co/spaces/Matthijs/speecht5-vc-demo">voice
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  <b>How to use:</b> Enter some English text and choose a speaker. The output is a mel spectrogram, which is converted to a mono 16 kHz waveform by the
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  HiFi-GAN vocoder. Because the model always applies random dropout, each attempt will give slightly different results.
 
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  """
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  article = """
@@ -86,7 +105,15 @@ gr.Interface(
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  fn=predict,
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  inputs=[
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  gr.Text(label="Input Text"),
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- gr.Radio(label="Speaker", choices=["BDL (male)", "CLB (female)", "RMS (male)", "SLT (female)"], value="BDL (male)"),
 
 
 
 
 
 
 
 
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  ],
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  outputs=[
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  gr.Audio(label="Generated Speech", type="numpy"),
 
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  speaker_embeddings = {
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  "BDL": "spkemb/cmu_us_bdl_arctic-wav-arctic_a0009.npy",
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  "CLB": "spkemb/cmu_us_clb_arctic-wav-arctic_a0144.npy",
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+ "KSP": "spkemb/cmu_us_ksp_arctic-wav-arctic_b0087.npy",
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  "RMS": "spkemb/cmu_us_rms_arctic-wav-arctic_b0353.npy",
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  "SLT": "spkemb/cmu_us_slt_arctic-wav-arctic_a0508.npy",
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  }
 
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  inputs = processor(text=text, return_tensors="pt")
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+ if speaker == "Surprise Me!":
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+ # load one of the provided speaker embeddings at random
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+ idx = np.random.randint(len(speaker_embeddings))
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+ key = list(speaker_embeddings.keys())[idx]
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+ speaker_embedding = np.load(speaker_embeddings[key])
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+
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+ # randomly shuffle the elements
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+ np.random.shuffle(speaker_embedding)
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+
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+ # randomly flip half the values
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+ x = (np.random.rand(512) >= 0.5) * 1.0
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+ x[x == 0] = -1.0
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+ speaker_embedding *= x
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+
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+ #speaker_embedding = np.random.rand(512).astype(np.float32) * 0.3 - 0.15
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+ else:
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+ speaker_embedding = np.load(speaker_embeddings[speaker[:3]])
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+
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  speaker_embedding = torch.tensor(speaker_embedding).unsqueeze(0)
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  speech = model.generate_speech(inputs["input_ids"], speaker_embedding, vocoder=vocoder)
 
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  <b>How to use:</b> Enter some English text and choose a speaker. The output is a mel spectrogram, which is converted to a mono 16 kHz waveform by the
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  HiFi-GAN vocoder. Because the model always applies random dropout, each attempt will give slightly different results.
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+ The <em>Surprise Me!</em> option creates a completely randomized speaker.
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  """
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  article = """
 
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  fn=predict,
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  inputs=[
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  gr.Text(label="Input Text"),
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+ gr.Radio(label="Speaker", choices=[
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+ "BDL (male)",
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+ "CLB (female)",
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+ "KSP (male)",
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+ "RMS (male)",
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+ "SLT (female)",
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+ "Surprise Me!"
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+ ],
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+ value="BDL (male)"),
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  ],
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  outputs=[
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  gr.Audio(label="Generated Speech", type="numpy"),
spkemb/cmu_us_awb_arctic-wav-arctic_a0002.npy ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:5db7a684ab490f21cec1628e00d461a184e369fe4eafb1ee441a796faf4ab6ae
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+ size 2176
spkemb/cmu_us_ksp_arctic-wav-arctic_b0087.npy ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:f6c5c2a38c2e400179019c560a74c4322f4ee13beda22ee601807545edee283e
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+ size 2176