HiFi-GAN developer on retainer: custom FastSpeech 2 mel spectrograms produce periodic 11 kHz tonal birdsong artifact absent with original LJSpeech-compatible spectrograms — mel spectrogram normalization scale mismatch triggers high-frequency sub-band generator resonance — normalizing custom mel output to HiFi-GAN expected range eliminates artifact — 3 weeks — HiFi-GAN developer on monthly retainer

HiFi-GAN (jik876/hifi-gan) GAN-based neural vocoder • Python 3.11; PyTorch 2.1.x • real-time TTS pipeline replacing WaveGlow • clean synthesis for all standard FastSpeech 2 mel spectrograms; introduces a periodic 11 kHz tone (audible as a faint “birdsong” artifact) on mel spectrograms generated by a custom-trained FastSpeech 2 model trained on a studio-quality recording corpus with a different mel spectrogram normalization scale; artifact not present when HiFi-GAN is used with the original LJSpeech-compatible FastSpeech 2; developer checks HiFi-GAN multi-period discriminator, multi-scale discriminator, generator architecture, and input normalization for 3 weeks • root cause: HiFi-GAN’s MPD/MSD was trained on mel spectrograms normalized to [−1, 1] with a specific max_magnitude parameter; custom FastSpeech 2 model uses different mel normalization producing values in [−2.5, 2.5]; HiFi-GAN generator encounters out-of-distribution mel input scale; in high-frequency sub-bands above 10 kHz the generator’s learned mapping has a periodic resonance at 11 kHz normally below the perceptible threshold for in-distribution inputs but audible when mel energy scale is 2.5× expected • fix: normalize custom FastSpeech 2 mel output to HiFi-GAN’s expected [−1, 1] range using HiFi-GAN training statistics • 11 kHz birdsong artifact: present on all custom-model outputs → eliminated • MOS: 3.5 → 4.0 • 1.5h retainer engagement

Eastfield HiFi-GAN Ltd. built a high-quality TTS pipeline for a voice content platform serving audiobook narration and documentary voiceovers. The pipeline replaced a WaveGlow vocoder with HiFi-GAN to improve synthesis quality and reduce inference latency. The team trained a custom FastSpeech 2 acoustic model on their studio-quality proprietary corpus — professional studio recordings with high dynamic range, clean room acoustics, and a different normalization scheme than the LJSpeech dataset used to train the HiFi-GAN checkpoint.

When they connected the custom FastSpeech 2 acoustic model to the HiFi-GAN vocoder, the speech quality was mostly excellent — better than the WaveGlow vocoder on most metrics. However, a subset of listeners during quality review noticed a faint, periodic high-frequency tone in the synthesized audio that was not present in the reference recordings or in synthesis with the original LJSpeech-trained FastSpeech 2 model. The tone was consistently around 11 kHz and had a periodic character — not a constant sine wave but a brief repeated chirp on voiced segments, like a distant bird call overlaid on the speech. The artifact was present on essentially all synthesized utterances from the custom FastSpeech 2 model and absent on all utterances from the original LJSpeech-compatible model run through the same HiFi-GAN vocoder.

The team initially suspected a discriminator training issue — perhaps the GAN training had converged to a mode that introduced this artifact. After verifying that the checkpoint was identical to the released version and finding no anomaly in the discriminator architecture, they investigated the acoustic model outputs. After three weeks of investigation, they engaged a HiFi-GAN retainer developer who identified the mel normalization scale mismatch as the root cause.

The mel spectrogram scale comparison at the time of the engagement:

# Eastfield HiFi-GAN Ltd. — HiFi-GAN 11 kHz artifact diagnostic
# HiFi-GAN (jik876/hifi-gan) GAN-based neural vocoder
# Python 3.11, PyTorch 2.1.x
# Deployment: real-time TTS pipeline replacing WaveGlow

import torch
import numpy as np
import librosa
from hifi_gan import HiFiGAN  # jik876/hifi-gan

# Load HiFi-GAN V1 checkpoint (trained on LJSpeech)
hifi_gan = HiFiGAN.from_pretrained('v1')
hifi_gan.eval()

# --- DIAGNOSTIC: compare mel spectrogram scale distributions ---

# 1. Compute mel normalization statistics for the reference LJSpeech training data
#    (these are the implicit expected statistics for the HiFi-GAN V1 checkpoint)
ljspeech_mel_stats = {
    'mean': -4.5,       # approximate LJSpeech mel mean (pre-normalization)
    'std': 3.9,         # approximate LJSpeech mel std (pre-normalization)
    'max_abs_normalized': 1.0,  # LJSpeech normalizes to [-1, 1] range
}

# 2. Compute mel stats for the custom studio training corpus
import glob
custom_corpus_mels = []
for audio_file in glob.glob('studio_corpus/*.wav')[:200]:  # sample 200 files
    audio, sr = librosa.load(audio_file, sr=22050)
    mel = librosa.feature.melspectrogram(
        y=audio, sr=sr, n_fft=1024, hop_length=256, win_length=1024,
        n_mels=80, fmin=0, fmax=8000
    )
    mel_db = librosa.power_to_db(mel)
    custom_corpus_mels.append(mel_db)

custom_corpus_mels = np.concatenate([m.flatten() for m in custom_corpus_mels])
custom_mel_stats = {
    'mean': float(np.mean(custom_corpus_mels)),
    'std': float(np.std(custom_corpus_mels)),
    'min': float(np.min(custom_corpus_mels)),
    'max': float(np.max(custom_corpus_mels)),
}

print("LJSpeech mel stats (HiFi-GAN training expected):")
print(f"  Normalized range: [-1.0, 1.0]")

print("\nCustom studio corpus mel stats (before normalization fix):")
print(f"  Mean: {custom_mel_stats['mean']:.2f}")
print(f"  Std:  {custom_mel_stats['std']:.2f}")
print(f"  Min:  {custom_mel_stats['min']:.2f}")
print(f"  Max:  {custom_mel_stats['max']:.2f}")

# Custom FastSpeech 2 mel normalization (studio corpus):
# After custom FS2's normalization step (not matching LJSpeech statistics):
custom_fs2_normalized_range = {
    'approx_min': -2.5,  # measured from custom FS2 mel output
    'approx_max':  2.5,  # 2.5× the HiFi-GAN expected [-1, 1] range
}
print(f"\nCustom FS2 normalized mel output range: "
      f"[{custom_fs2_normalized_range['approx_min']}, {custom_fs2_normalized_range['approx_max']}]")
print("Scale factor vs HiFi-GAN expected: 2.5×  ← BUG: out-of-distribution mel input")

# 3. Confirm artifact frequency via spectral analysis
test_text = "The session recording quality meets broadcast standards for all channels."
mel_original = get_ljspeech_fastspeech2_mel(test_text)   # LJSpeech-compatible FS2
mel_custom   = get_custom_fastspeech2_mel(test_text)     # Custom studio FS2

audio_original = hifi_gan.infer(mel_original)
audio_custom   = hifi_gan.infer(mel_custom)

# FFT power spectra
def spectral_power_at_freq(audio: np.ndarray, sr: int, target_hz: float,
                            bandwidth_hz: float = 200) -> float:
    """Measure spectral power in a band around target_hz, in dBFS."""
    fft = np.abs(np.fft.rfft(audio, n=sr))  # 1-second FFT for 1 Hz resolution
    freqs = np.fft.rfftfreq(sr, d=1.0/sr)
    band_mask = np.abs(freqs - target_hz) < bandwidth_hz / 2
    band_power = np.mean(fft[band_mask] ** 2)
    return float(10 * np.log10(band_power + 1e-12))

power_11khz_original = spectral_power_at_freq(audio_original, 22050, 11000)
power_11khz_custom   = spectral_power_at_freq(audio_custom,   22050, 11000)
power_fundamental    = spectral_power_at_freq(audio_custom,   22050,   200)  # ~200 Hz fundamental

print(f"\n11 kHz spectral power (original LJSpeech FS2):  {power_11khz_original:.1f} dBFS")
print(f"11 kHz spectral power (custom studio FS2):       {power_11khz_custom:.1f} dBFS")
print(f"Fundamental (~200 Hz) power (custom FS2):        {power_fundamental:.1f} dBFS")
print(f"11 kHz / fundamental ratio (custom FS2):         {power_11khz_custom - power_fundamental:.1f} dB")

# Output:
# LJSpeech mel stats (HiFi-GAN training expected): Normalized range: [-1.0, 1.0]
# Custom studio corpus mel stats (before normalization fix):
#   Mean: -6.2   Std: 5.8   Min: -18.4   Max: 7.3
# Custom FS2 normalized mel output range: [-2.5, 2.5]  ← 2.5× expected
#
# 11 kHz spectral power (original LJSpeech FS2):  -61.3 dBFS  ← below perceptible
# 11 kHz spectral power (custom studio FS2):       -38.7 dBFS  ← AUDIBLE (> -45 dBFS threshold)
# Fundamental (~200 Hz) power (custom FS2):        -19.4 dBFS
# 11 kHz / fundamental ratio (custom FS2):         -19.3 dB   ← artifact audible relative to speech

Why HiFi-GAN’s high-frequency sub-band generator resonance becomes audible at 2.5× the expected mel input scale

HiFi-GAN’s generator uses a stack of transposed convolutional layers with multi-receptive field fusion to map mel spectrogram frames to waveform samples. Each layer in the generator processes its input through learned nonlinear transformations, and the high-frequency output sub-bands — those corresponding to frequencies above 8 to 10 kHz — are generated by the generator’s processing of mel frequency bins that capture high-frequency acoustic energy. In natural speech from the LJSpeech training corpus, these high-frequency mel bins have relatively low energy compared to the fundamental and first few formant frequencies.

When the generator was trained with adversarial training against the multi-period and multi-scale discriminators on LJSpeech mel spectrograms, the high-frequency sub-band generator layers converged to a learned mapping that produces realistic high-frequency noise characteristics when given in-distribution mel inputs. This mapping has a subtle periodic resonance at approximately 11 kHz — a characteristic of the learned filter bank weights where certain combinations of filter coefficients create a constructive interference at that frequency. For in-distribution mel inputs in the [-1, 1] range, the energy contribution from this resonance is approximately -61 dBFS, well below the perceptible threshold for a tone relative to the speech fundamental at -19 dBFS. The 42 dB difference between the resonance and the fundamental means listeners hear only the speech content.

When the custom FastSpeech 2 model feeds mel spectrograms in the [-2.5, 2.5] range, the generator’s high-frequency sub-band layers receive inputs 2.5 times larger than their calibrated operating range. The same learned filter coefficients that produce a -61 dBFS resonance at unit scale produce a resonance at 20 log10(2.5) ≈ 8 dB higher — approximately -53 dBFS. The speech fundamental energy also scales with the input, but not linearly in the high-frequency sub-bands due to the nonlinear activations. The net result is that the 11 kHz resonance rises from -61 dBFS to approximately -39 dBFS, now only 20 dB below the fundamental rather than 42 dB, which crosses the perceptibility threshold for trained listeners in a quiet listening environment.

# Fix: normalize custom FastSpeech 2 mel output to HiFi-GAN expected [-1, 1] range
# Apply HiFi-GAN training statistics (computed from LJSpeech or the HiFi-GAN checkpoint)

import numpy as np
import torch

# HiFi-GAN V1 (LJSpeech) mel normalization statistics
# These are the statistics used during HiFi-GAN training
HIFIGAN_MEL_MEAN = -4.5    # LJSpeech mel mean (dB scale before normalization)
HIFIGAN_MEL_STD  = 3.9     # LJSpeech mel std

# Custom FastSpeech 2 mel normalization statistics (from studio corpus measurement)
CUSTOM_FS2_MEAN = -6.2
CUSTOM_FS2_STD  = 5.8


def normalize_mel_for_hifigan(mel: torch.Tensor,
                               source_mean: float, source_std: float,
                               target_mean: float = HIFIGAN_MEL_MEAN,
                               target_std: float = HIFIGAN_MEL_STD) -> torch.Tensor:
    """
    Rescale mel spectrogram from source distribution to HiFi-GAN expected distribution.
    1. Undo source normalization to recover dB-scale mel values.
    2. Apply HiFi-GAN target normalization.
    Result: mel values in approximately the same range as LJSpeech training mels.
    """
    # Step 1: undo custom FS2 normalization (recover raw dB mel values)
    mel_db = mel * source_std + source_mean

    # Step 2: apply HiFi-GAN training normalization
    mel_normalized = (mel_db - target_mean) / target_std

    return mel_normalized


# Integration: add normalization step between FastSpeech 2 and HiFi-GAN

def synthesize_with_normalized_mel(fs2_model, hifigan_model, text: str,
                                    custom_mean: float = CUSTOM_FS2_MEAN,
                                    custom_std: float = CUSTOM_FS2_STD) -> np.ndarray:
    """
    Synthesize text using custom FastSpeech 2 + HiFi-GAN with mel normalization fix.
    """
    with torch.no_grad():
        # Step 1: get mel spectrogram from custom FastSpeech 2
        mel_custom = fs2_model.synthesize(text)  # shape: (n_mels, T), range ~[-2.5, 2.5]

        # Step 2: rescale to HiFi-GAN expected range
        mel_hifigan = normalize_mel_for_hifigan(mel_custom, custom_mean, custom_std)
        # mel_hifigan now approximately in [-1.0, 1.0] range

        # Step 3: HiFi-GAN vocoder
        audio = hifigan_model.infer(mel_hifigan)

    return audio.numpy()


# --- Results after mel normalization fix ---
#
# 11 kHz spectral power (custom FS2, AFTER normalization fix):  -60.8 dBFS
# 11 kHz spectral power (custom FS2, before fix):               -38.7 dBFS
# 11 kHz / fundamental ratio (after fix):                        -41.4 dB  ← below perceptible threshold
#
# MOS evaluation (10 listeners, 50 utterances each condition):
#   Before fix: MOS 3.5 (σ=0.6) — "slightly annoying high-pitched chirp on some voices"
#   After fix:  MOS 4.0 (σ=0.3) — "clean, no artifacts"
#
# Artifact detection (spectral peak > -45 dBFS at 11 kHz ± 500 Hz):
#   Before fix: 100% of utterances from custom FS2 model
#   After fix:   0% of utterances from custom FS2 model
#
# Regression check (original LJSpeech FS2 — verify no degradation):
#   Before:  11 kHz power -61.3 dBFS  ← clean
#   After applying normalization with correct LJSpeech statistics:
#             11 kHz power -61.1 dBFS  ← no change (identity transform for in-distribution input)
#
# Key insight: the normalization transform is a simple affine rescaling.
# It does not modify the acoustic content of the mel spectrogram —
# only the scale. This makes it a zero-risk, zero-side-effect fix
# when the source and target statistics are measured accurately.

print("Fix: normalize custom FastSpeech 2 mel output to HiFi-GAN expected distribution.")
print("11 kHz artifact: present on all custom-model outputs → eliminated.")
print("MOS: 3.5 → 4.0; 1.5h")

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