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Running
Matthijs Hollemans
commited on
Commit
โข
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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}
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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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-
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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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@@ -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 = """
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@@ -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=[
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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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# randomly shuffle the elements
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np.random.shuffle(speaker_embedding)
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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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#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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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"),
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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
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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
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