benchmarks/rnnt/ootb/inference/pytorch/parts/segment.py [34:81]:
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        samples = self._convert_samples_to_float32(samples)
        if target_sr is not None and target_sr != sample_rate:
            samples = librosa.core.resample(samples, sample_rate, target_sr)
            sample_rate = target_sr
        if trim:
            samples, _ = librosa.effects.trim(samples, trim_db)
        self._samples = samples
        self._sample_rate = sample_rate
        if self._samples.ndim >= 2:
            self._samples = np.mean(self._samples, 1)

    def __eq__(self, other):
        """Return whether two objects are equal."""
        if type(other) is not type(self):
            return False
        if self._sample_rate != other._sample_rate:
            return False
        if self._samples.shape != other._samples.shape:
            return False
        if np.any(self.samples != other._samples):
            return False
        return True

    def __ne__(self, other):
        """Return whether two objects are unequal."""
        return not self.__eq__(other)

    def __str__(self):
        """Return human-readable representation of segment."""
        return ("%s: num_samples=%d, sample_rate=%d, duration=%.2fsec, "
                "rms=%.2fdB" % (type(self), self.num_samples, self.sample_rate,
                                self.duration, self.rms_db))

    @staticmethod
    def _convert_samples_to_float32(samples):
        """Convert sample type to float32.
        Audio sample type is usually integer or float-point.
        Integers will be scaled to [-1, 1] in float32.
        """
        float32_samples = samples.astype('float32')
        if samples.dtype in np.sctypes['int']:
            bits = np.iinfo(samples.dtype).bits
            float32_samples *= (1. / 2 ** (bits - 1))
        elif samples.dtype in np.sctypes['float']:
            pass
        else:
            raise TypeError("Unsupported sample type: %s." % samples.dtype)
        return float32_samples
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benchmarks/rnnt/ootb/train/common/audio.py [68:116]:
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        samples = self._convert_samples_to_float32(samples)
        if target_sr is not None and target_sr != sample_rate:
            samples = librosa.core.resample(samples, sample_rate, target_sr)
            sample_rate = target_sr
        if trim:
            samples, _ = librosa.effects.trim(samples, trim_db)
        self._samples = samples
        self._sample_rate = sample_rate
        if self._samples.ndim >= 2:
            self._samples = np.mean(self._samples, 1)

    def __eq__(self, other):
        """Return whether two objects are equal."""
        if type(other) is not type(self):
            return False
        if self._sample_rate != other._sample_rate:
            return False
        if self._samples.shape != other._samples.shape:
            return False
        if np.any(self.samples != other._samples):
            return False
        return True

    def __ne__(self, other):
        """Return whether two objects are unequal."""
        return not self.__eq__(other)

    def __str__(self):
        """Return human-readable representation of segment."""
        return ("%s: num_samples=%d, sample_rate=%d, duration=%.2fsec, "
                        "rms=%.2fdB" % (type(self), self.num_samples, self.sample_rate,
                                                        self.duration, self.rms_db))

    @staticmethod
    def _convert_samples_to_float32(samples):
        """Convert sample type to float32.

        Audio sample type is usually integer or float-point.
        Integers will be scaled to [-1, 1] in float32.
        """
        float32_samples = samples.astype('float32')
        if samples.dtype in np.sctypes['int']:
            bits = np.iinfo(samples.dtype).bits
            float32_samples *= (1. / 2 ** (bits - 1))
        elif samples.dtype in np.sctypes['float']:
            pass
        else:
            raise TypeError("Unsupported sample type: %s." % samples.dtype)
        return float32_samples
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