How to use the pydub.utils.db_to_float function in pydub

fade_frames = end_frame - start_frame
            scale_step = gain_delta / fade_frames

            for i in range(int(fade_frames)):
                volume_change = from_power + (scale_step * i)
                sample = self.get_frame(int(start_frame + i))
                sample = audioop.mul(sample, self.sample_width, volume_change)

                output.append(sample)

        # original data after the crossfade portion, at the new volume
        after_fade = self[end:]._data
        if to_gain != 0:
            after_fade = audioop.mul(after_fade,
                                     self.sample_width,
                                     db_to_float(to_gain))
        output.append(after_fade)

        return self._spawn(data=output)

def normalize(seg, headroom=0.1):
    """
    headroom is how close to the maximum volume to boost the signal up to (specified in dB)
    """
    peak_sample_val = seg.max
    
    # if the max is 0, this audio segment is silent, and can't be normalized
    if peak_sample_val == 0:
        return seg
    
    target_peak = seg.max_possible_amplitude * db_to_float(-headroom)

    needed_boost = ratio_to_db(target_peak / peak_sample_val)
    return seg.apply_gain(needed_boost)

fade_frames = end_frame - start_frame
            scale_step = gain_delta / fade_frames

            for i in range(int(fade_frames)):
                volume_change = from_power + (scale_step * i)
                sample = self.get_frame(int(start_frame + i))
                sample = audioop.mul(sample, self.sample_width, volume_change)

                output.append(sample)

        # original data after the crossfade portion, at the new volume
        after_fade = self[end:]._data
        if to_gain != 0:
            after_fade = audioop.mul(after_fade,
                                     self.sample_width,
                                     db_to_float(to_gain))
        output.append(after_fade)

        return self._spawn(data=output)

def apply_gain(self, volume_change):
        return self._spawn(data=audioop.mul(self._data, self.sample_width,
                                            db_to_float(float(volume_change))))

def detect_silence(audio_segment, min_silence_len=1000, silence_thresh=-16, seek_step=1):
    seg_len = len(audio_segment)

    # you can't have a silent portion of a sound that is longer than the sound
    if seg_len < min_silence_len:
        return []

    # convert silence threshold to a float value (so we can compare it to rms)
    silence_thresh = db_to_float(silence_thresh) * audio_segment.max_possible_amplitude

    # find silence and add start and end indicies to the to_cut list
    silence_starts = []

    # check successive (1 sec by default) chunk of sound for silence
    # try a chunk at every "seek step" (or every chunk for a seek step == 1)
    last_slice_start = seg_len - min_silence_len
    slice_starts = range(0, last_slice_start + 1, seek_step)

    # guarantee last_slice_start is included in the range
    # to make sure the last portion of the audio is seached
    if last_slice_start % seek_step:
        slice_starts = itertools.chain(slice_starts, [last_slice_start])

    for i in slice_starts:
        audio_slice = audio_segment[i:i + min_silence_len]

pos = 0
        seg1_len = len(seg1)
        seg2_len = len(seg2)
        while times:
            remaining = max(0, seg1_len - pos)
            if seg2_len >= remaining:
                seg2 = seg2[:remaining]
                seg2_len = remaining
                # we've hit the end, we're done looping (if we were) and this
                # is our last go-around
                times = 1

            if gain_during_overlay:
                seg1_overlaid = seg1[pos:pos + seg2_len]
                seg1_adjusted_gain = audioop.mul(seg1_overlaid, self.sample_width,
                                               db_to_float(float(gain_during_overlay)))
                output.write(audioop.add(seg1_adjusted_gain, seg2, sample_width))
            else:
                output.write(audioop.add(seg1[pos:pos + seg2_len], seg2,
                                     sample_width))
            pos += seg2_len

            # dec times to break our while loop (eventually)
            times -= 1

        output.write(seg1[pos:])

        return spawn(data=output)

def detect_silence(audio_segment, min_silence_len=1000, silence_thresh=-16, seek_step=1):
    seg_len = len(audio_segment)

    # you can't have a silent portion of a sound that is longer than the sound
    if seg_len < min_silence_len:
        return []

    # convert silence threshold to a float value (so we can compare it to rms)
    silence_thresh = db_to_float(silence_thresh) * audio_segment.max_possible_amplitude

    # find silence and add start and end indicies to the to_cut list
    silence_starts = []

    # check successive (1 sec by default) chunk of sound for silence
    # try a chunk at every "seek step" (or every chunk for a seek step == 1)
    last_slice_start = seg_len - min_silence_len
    slice_starts = range(0, last_slice_start + 1, seek_step)

    # guarantee last_slice_start is included in the range
    # to make sure the last portion of the audio is seached
    if last_slice_start % seek_step:
        slice_starts = itertools.chain(slice_starts, [last_slice_start])

    for i in slice_starts:
        audio_slice = audio_segment[i:i + min_silence_len]

start += len(self)
        if end is not None and end < 0:
            end += len(self)

        if duration is not None and duration < 0:
            raise InvalidDuration("duration must be a positive integer")

        if duration:
            if start is not None:
                end = start + duration
            elif end is not None:
                start = end - duration
        else:
            duration = end - start

        from_power = db_to_float(from_gain)

        output = []

        # original data - up until the crossfade portion, as is
        before_fade = self[:start]._data
        if from_gain != 0:
            before_fade = audioop.mul(before_fade,
                                      self.sample_width,
                                      from_power)
        output.append(before_fade)

        gain_delta = db_to_float(to_gain) - from_power

        # fades longer than 100ms can use coarse fading (one gain step per ms),
        # shorter fades will have audible clicks so they use precise fading
        #(one gain step per sample)

def apply_gain_stereo(seg, left_gain=0.0, right_gain=0.0):
    """
    left_gain - amount of gain to apply to the left channel (in dB)
    right_gain - amount of gain to apply to the right channel (in dB)
    
    note: mono audio segments will be converted to stereo
    """
    if seg.channels == 1:
        left = right = seg
    elif seg.channels == 2:
        left, right = seg.split_to_mono()
    
    l_mult_factor = db_to_float(left_gain)
    r_mult_factor = db_to_float(right_gain)
    
    left_data = audioop.mul(left._data, left.sample_width, l_mult_factor)
    left_data = audioop.tostereo(left_data, left.sample_width, 1, 0)
    
    right_data = audioop.mul(right._data, right.sample_width, r_mult_factor)
    right_data = audioop.tostereo(right_data, right.sample_width, 0, 1)
    
    output = audioop.add(left_data, right_data, seg.sample_width)
    
    return seg._spawn(data=output,
                overrides={'channels': 2,
                           'frame_width': 2 * seg.sample_width})

def to_audio_segment(self, duration=1000.0, volume=0.0):
        """
        Duration in milliseconds
            (default: 1 second)
        Volume in DB relative to maximum amplitude
            (default 0.0 dBFS, which is the maximum value)
        """
        minval, maxval = get_min_max_value(self.bit_depth)
        sample_width = get_frame_width(self.bit_depth)
        array_type = get_array_type(self.bit_depth)

        gain = db_to_float(volume)
        sample_count = int(self.sample_rate * (duration / 1000.0))

        sample_data = (int(val * maxval * gain) for val in self.generate())
        sample_data = itertools.islice(sample_data, 0, sample_count)

        data = array.array(array_type, sample_data)
        
        try:
            data = data.tobytes()
        except:
            data = data.tostring()

        return AudioSegment(data=data, metadata={
            "channels": 1,
            "sample_width": sample_width,
            "frame_rate": self.sample_rate,