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abc.txt
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import streamlit as st
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from kokoro import KPipeline
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import soundfile as sf
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import io
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import os
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# Install espeak-ng if not installed
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if not os.system("which espeak-ng"):
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st.text("espeak-ng already installed.")
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else:
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os.system("apt-get -qq -y install espeak-ng")
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st.text("Installing espeak-ng...")
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# Streamlit App UI Setup
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st.title("Text-to-Speech with Kokoro")
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st.sidebar.header("Configuration & Instructions")
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# Sidebar Instructions
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st.sidebar.markdown("""
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### How to Use the Text-to-Speech App:
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1. **Enter Text**: In the main text area, input any text that you want the model to convert to speech.
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2. **Select Language**:
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- Choose the language of the text you are entering. Available options include:
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- ๐บ๐ธ American English (`a`)
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- ๐ฌ๐ง British English (`b`)
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- ๐ช๐ธ Spanish (`e`)
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- ๐ซ๐ท French (`f`)
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- ๐ฎ๐ณ Hindi (`h`)
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- ๐ฎ๐น Italian (`i`)
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- ๐ง๐ท Brazilian Portuguese (`p`)
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- ๐จ๐ณ Mandarin Chinese (`z`)
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- ๐ฏ๐ต Japanese (`j`)
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3. **Select Voice**:
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- Choose the voice style for the speech. You can pick different voices based on tone and gender, such as `af_heart`, `af_joy`, etc.
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4. **Adjust Speed**:
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- Use the speed slider to change how fast the speech is generated. You can set it between `0.5x` to `2.0x`, where `1.0x` is the normal speed.
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5. **Generate Speech**:
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- After configuring the settings, click on the **"Generate Audio"** button. The app will process your text and produce speech audio accordingly.
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6. **Download**:
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- Once the audio is generated, you can play it directly in the app or download it as a `.wav` file by clicking on the **"Download Audio"** button.
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Enjoy experimenting with the text-to-speech conversion, and feel free to try different voices, speeds, and languages!
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""")
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# User input for text, language, and voice settings
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input_text = st.text_area("Enter your text here", "The sky above the port was the color of television...")
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lang_code = st.selectbox("Select Language", ['a', 'b', 'e', 'f', 'h', 'i', 'p', 'z', 'j'])
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voice = st.selectbox("Select Voice", ['af_alloy', 'af_aoede', 'af_bella', 'af_heart', 'af_jessica', 'af_kore', 'af_nicole', 'af_nova', 'af_river', 'af_sarah', 'af_sky',
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'am_adam', 'am_echo', 'am_eric', 'am_fenrir', 'am_liam', 'am_michael', 'am_onyx', 'am_puck', 'am_santa',
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'bf_alice', 'bf_emma', 'bf_isabella', 'bf_lily',
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'bm_daniel', 'bm_fable', 'bm_george', 'bm_lewis',
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'ef_dora',
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'em_alex', 'em_santa',
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'ff_siwis',
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'hf_alpha', 'hf_beta',
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'hm_omega', 'hm_psi',
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'if_sara',
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'im_nicola',
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'jf_alpha', 'jf_gongitsune', 'jf_nezumi', 'jf_tebukuro',
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'jm_kumo',
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'pf_dora',
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'pm_alex', 'pm_santa',
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'zf_xiaobei', 'zf_xiaoni', 'zf_xiaoxiao', 'zf_xiaoyi',
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'zm_yunjian', 'zm_yunxi', 'zm_yunxia', 'zm_yunyang']
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) # Change voice options as per model
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speed = st.slider("Speed", min_value=0.5, max_value=2.0, value=1.0, step=0.1)
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# Initialize the TTS pipeline with user-selected language
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pipeline = KPipeline(lang_code=lang_code)
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# Generate Audio function
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def generate_audio(text, lang_code, voice, speed):
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generator = pipeline(text, voice=voice, speed=speed, split_pattern=r'\n+')
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for i, (gs, ps, audio) in enumerate(generator):
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audio_data = audio
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# Save audio to in-memory buffer
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buffer = io.BytesIO()
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# Explicitly specify format as WAV
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sf.write(buffer, audio_data, 24000, format='WAV') # Add 'format="WAV"'
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buffer.seek(0)
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return buffer
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# Generate and display the audio file
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if st.button('Generate Audio'):
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st.write("Generating speech...")
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audio_buffer = generate_audio(input_text, lang_code, voice, speed)
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# Display Audio player in the app
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st.audio(audio_buffer, format='audio/wav')
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# Optional: Save the generated audio file for download
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st.download_button(
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label="Download Audio",
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data=audio_buffer,
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file_name="generated_speech.wav",
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mime="audio/wav"
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)
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