Ai model ekak

Yeezus

Well-known member
  • Nov 20, 2024
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    mchanla mata oni audio ekak wachana ain karnna ex: fuck vage explicit words AI use karla. prompt eka type karama wachna ai eken ain karala output enna. ubala danna ai models or sites tiyei da. mama danata dakka ekama site eka https://songcleaner.com/ but meke editing quality madi. dawskata 2i free tiyenne. @Nidarshana_k @රෝසි ආච්චි @infernocus @olu bakka
    Gemini 3.7 flash use කරලා tool එකක් හදාගන්න.
    මේ අවුරුද්ද ඉවර වෙනකල් 50% discount එකක් යනවා token usage.

    Open router ගත්තොත් තව 50% ආසන්න ගානක් අඩු වෙනවා ටිකක්.බලන්න. model එක release කරෙත් ඊයේ.
     
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    baba-chuti

    Well-known member
  • Feb 27, 2017
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    ethakota ethana wachane wenuwata mkkda danne?
    e kalla auto mute wenawa background music play wenne. dj karna kattiya num podi shift karnawa first letter eke prounce karnawa ekkama. uba english song wala clean version ahala tiyei da meeta kalin? anna e vage pattern ekak.
    Gemini 3.7 flash use කරලා tool එකක් හදාගන්න.
    මේ අවුරුද්ද ඉවර වෙනකල් 50% discount එකක් යනවා token usage.

    Open router ගත්තොත් තව 50% ආසන්න ගානක් අඩු වෙනවා ටිකක්.බලන්න. model එක release කරෙත් ඊයේ.
    Gemini 3.7 flash use කරලා tool එකක් හදාගන්න. -- ma gawa antigravity eka tiyei. meka try kala hari yanne na machan. songcleaner ugema model ekak tiyenne man hithanne. mata oni thridpary ekak
     

    helplesser

    Well-known member
  • Nov 20, 2017
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    Mirihana
    alis.alberta.ca
    Python:
    from faster_whisper import WhisperModel
    from pydub import AudioSegment
    from pydub.generators import Sine
    
    TARGETS = {"damn", "hell"}
    PAD_MS = 60  # timestamps are slightly loose; pad both sides
    
    model = WhisperModel("small", compute_type="int8")
    segments, _ = model.transcribe("input.mp3", word_timestamps=True)
    audio = AudioSegment.from_file("input.mp3")
    
    spans = [
        (int(w.start * 1000) - PAD_MS, int(w.end * 1000) + PAD_MS)
        for seg in segments for w in seg.words
        if w.word.strip().lower().strip(".,!?'\"") in TARGETS
    ]
    
    for start, end in spans:
        start, end = max(0, start), min(len(audio), end)
        patch = Sine(1000).to_audio_segment(duration=end - start).apply_gain(-12)
        # or: patch = AudioSegment.silent(duration=end - start)
        audio = audio[:start] + patch + audio[end:]
    
    audio.export("output.mp3", format="mp3")
     
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    baba-chuti

    Well-known member
  • Feb 27, 2017
    7,228
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    Python:
    from faster_whisper import WhisperModel
    from pydub import AudioSegment
    from pydub.generators import Sine
    
    TARGETS = {"damn", "hell"}
    PAD_MS = 60  # timestamps are slightly loose; pad both sides
    
    model = WhisperModel("small", compute_type="int8")
    segments, _ = model.transcribe("input.mp3", word_timestamps=True)
    audio = AudioSegment.from_file("input.mp3")
    
    spans = [
        (int(w.start * 1000) - PAD_MS, int(w.end * 1000) + PAD_MS)
        for seg in segments for w in seg.words
        if w.word.strip().lower().strip(".,!?'\"") in TARGETS
    ]
    
    for start, end in spans:
        start, end = max(0, start), min(len(audio), end)
        patch = Sine(1000).to_audio_segment(duration=end - start).apply_gain(-12)
        # or: patch = AudioSegment.silent(duration=end - start)
        audio = audio[:start] + patch + audio[end:]
    
    audio.export("output.mp3", format="mp3")
    try ekak deela balnnum.
     

    olu bakka

    Well-known member
  • Aug 18, 2011
    23,007
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    ඕකට ලේසිම වැඩේ මුලින්ම single word වලට timestamps එක්ක transcribe කරපන් audio එක. ඊට පස්සෙ ඉතින් මහ දෙයක් නෑ අදාල වචනෙ තියෙන timestamp එක දන්නවනෙ. එතනට mute effect එකක් දාගන්න program කරගනින්.