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| import spaces | |
| import tempfile | |
| import gradio as gr | |
| import os | |
| from whisperspeech.pipeline import Pipeline | |
| import torch | |
| import soundfile as sf | |
| import numpy as np | |
| import torch.nn.functional as F | |
| from whisperspeech.languages import LANGUAGES | |
| from whisperspeech.pipeline import Pipeline | |
| title = """#🙋🏻♂️ Welcome to🌟Tonic's🌬️💬📝WhisperSpeech | |
| You can use this ZeroGPU Space to test out the current model [🌬️💬📝collabora/whisperspeech](https://huggingface.co/collabora/whisperspeech). 🌬️💬📝collabora/whisperspeech is An Open Source text-to-speech system built by inverting Whisper. Previously known as spear-tts-pytorch. It's like Stable Diffusion but for speech – both powerful and easily customizable. | |
| You can also use 🌬️💬📝WhisperSpeech by cloning this space. 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/laion-whisper?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> | |
| Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community 👻 [](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [Poly](https://github.com/tonic-ai/poly) 🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗 | |
| """ | |
| def whisper_speech_demo(text, lang, speaker_audio=None, mix_lang=None, mix_text=None): | |
| pipe = Pipeline() | |
| speaker_url = None | |
| if speaker_audio is not None: | |
| speaker_url = speaker_audio.name | |
| if mix_lang and mix_text: | |
| mixed_langs = lang.split(',') + mix_lang.split(',') | |
| mixed_texts = [text] + mix_text.split(',') | |
| stoks = pipe.t2s.generate(mixed_texts, lang=mixed_langs) | |
| audio_data = pipe.generate(stoks, speaker_url, lang=mixed_langs[0]) | |
| else: | |
| audio_data = pipe.generate(text, speaker_url, lang) | |
| with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file: | |
| tmp_file_name = tmp_file.name | |
| audio_np = audio_data.numpy() # Convert tensor to numpy array | |
| sf.write(tmp_file_name, audio_np, 22050) # Assuming a sample rate of 22050 Hz | |
| return tmp_file_name | |
| with gr.Blocks() as demo: | |
| gr.Markdown(title) | |
| with gr.Row(): | |
| text_input = gr.Textbox(label="Enter text") | |
| lang_input = gr.Dropdown(choices=list(LANGUAGES.keys()), label="Language") | |
| speaker_input = gr.Audio(label="Upload or Record Speaker Audio (optional)", sources=["upload", "microphone"], type="filepath") | |
| with gr.Row(): | |
| mix_lang_input = gr.CheckboxGroup(choices=list(LANGUAGES.keys()), label="Mixed Languages (optional)") | |
| mix_text_input = gr.Textbox(label="Mixed Texts (optional, for mixed languages)", placeholder="e.g., Hello, Cześć") | |
| with gr.Row(): | |
| submit_button = gr.Button("Generate Speech") | |
| output_audio = gr.Audio(label="🌬️💬📝WhisperSpeech") | |
| submit_button.click( | |
| whisper_speech_demo, | |
| inputs=[text_input, lang_input, speaker_input, mix_lang_input, mix_text_input], | |
| outputs=output_audio | |
| ) | |
| demo.launch() |