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Running
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T4
Update app.py
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app.py
CHANGED
@@ -21,7 +21,8 @@ You can also use 🌬️💬📝WhisperSpeech by cloning this space. 🧬🔬
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We're **celebrating the release of the whisperspeech** at [the LAION community, if you love open source ai learn more here : https://laion.ai/](https://laion.ai/) big thanks to the folks at huggingface for the community grant 🤗
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### How to Use
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Input text with the language identifiers provided to create a multilingual speech. Optionally you can add an audiosample to make a voice print.
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"""
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# text examples=["<en> Hello, how are you? <fr> Bonjour, comment ça va?", "<de> Guten Tag <it> Buongiorno <jp> こんにちは"]
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@@ -46,9 +47,11 @@ def generate_segment_audio(text, lang, speaker_url, pipe):
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audio_np = audio_data_resampled.cpu().numpy()
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return audio_np
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def concatenate_audio_segments(segments):
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concatenated_audio = np.concatenate(padded_segments, axis=0)
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concatenated_audio = concatenated_audio / np.max(np.abs(concatenated_audio))
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return np.asarray(concatenated_audio, dtype=np.float32)
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We're **celebrating the release of the whisperspeech** at [the LAION community, if you love open source ai learn more here : https://laion.ai/](https://laion.ai/) big thanks to the folks at huggingface for the community grant 🤗
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### How to Use
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Input text with the language identifiers provided to create a multilingual speech. Optionally you can add an audiosample to make a voice print.Scroll down and try the api <3 Gradio.
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This space runs on ZeroGPU, so **you need to be patient** while you acquire the GPU and load the model the first time you make a request !
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"""
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# text examples=["<en> Hello, how are you? <fr> Bonjour, comment ça va?", "<de> Guten Tag <it> Buongiorno <jp> こんにちは"]
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audio_np = audio_data_resampled.cpu().numpy()
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return audio_np
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# this function pads each segment to the length of the longest segment which is not optimal
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def concatenate_audio_segments(segments):
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mono_segments = [seg[:, 0] if seg.ndim > 1 else seg for seg in segments]
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max_len = max(seg.shape[0] for seg in mono_segments)
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padded_segments = [np.pad(seg, (0, max_len - seg.shape[0]), 'constant') for seg in mono_segments]
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concatenated_audio = np.concatenate(padded_segments, axis=0)
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concatenated_audio = concatenated_audio / np.max(np.abs(concatenated_audio))
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return np.asarray(concatenated_audio, dtype=np.float32)
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