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Text-to-Speech (TTS) — From Tacotron to F5 and Kokoro ​

ASR inverts speech to text; TTS inverts text to speech. The 2026 stack is three parts: text → tokens, tokens → mel, mel → waveform. Each part has a default model that fits in a laptop.

Type: Build Languages: Python Prerequisites: Phase 6 · 02 (Spectrograms & Mel), Phase 5 · 09 (Seq2Seq), Phase 7 · 05 (Full Transformer) Time: ~75 minutes

The Problem ​

You have a string: "Please remind me to water the plants at 6 pm." You need a 3-second audio clip that sounds natural, has correct prosody (pauses, stress), pronounces "plants" with the right vowel, and runs in under 300 ms on a CPU for a live voice assistant. You also need to swap voices, handle code-switched input ("remind me at 6 pm, daijoubu?"), and not embarrass yourself on names.

Modern TTS pipelines look like this:

  1. Text frontend. Normalize text (dates, numbers, emails), convert to phonemes or subword tokens, predict prosody features.
  2. Acoustic model. Text → mel spectrogram. Tacotron 2 (2017), FastSpeech 2 (2020), VITS (2021), F5-TTS (2024), Kokoro (2024).
  3. Vocoder. Mel → waveform. WaveNet (2016), WaveRNN, HiFi-GAN (2020), BigVGAN (2022), neural codec vocoders in 2024+.

In 2026 the acoustic + vocoder split blurs with end-to-end diffusion and flow-matching models. But the mental model of three parts still holds for debugging.

The Concept ​

Tacotron, FastSpeech, VITS, F5/Kokoro side-by-side

Tacotron 2 (2017). Seq2seq: char-embedding → BiLSTM encoder → location-sensitive attention → autoregressive LSTM decoder emits mel frames. Slow (AR), wobbly on long text. Still cited as a baseline.

FastSpeech 2 (2020). Non-autoregressive. Duration predictor outputs how many mel frames each phoneme gets. 1-pass, 10× faster than Tacotron. Loses some naturalness (monotonic alignment) but ships everywhere.

VITS (2021). Jointly trains encoder + flow-based duration + HiFi-GAN vocoder end-to-end with variational inference. High quality, single model. Dominant open-source TTS 2022–2024. Variants: YourTTS (multi-speaker zero-shot), XTTS v2 (2024, Coqui).

F5-TTS (2024). Diffusion transformer over flow matching. Natural prosody, zero-shot voice cloning with 5 seconds of reference audio. Top of the 2026 open-source TTS leaderboards. 335M params.

Kokoro (2024). Small (82M), CPU-runnable, best-in-class English TTS for real-time use. Closed-vocabulary English-only, apache-2.0.

OpenAI TTS-1-HD, ElevenLabs v2.5, Google Chirp-3. Commercial state of the art. ElevenLabs v2.5 emotion tags ("[whispered]", "[laughing]") and character voices dominate audiobook production in 2026.

Vocoder evolution ​

EraVocoderLatencyQuality
2016WaveNetoffline onlySOTA at release
2018WaveRNN~realtimegood
2020HiFi-GAN100× realtimenear-human
2022BigVGAN50× realtimegeneralizes across speakers/langs
2024SNAC, DAC (neural codecs)integrated with AR modelsdiscrete tokens, bit-efficient

By 2026 most "TTS" models are end-to-end from text to waveform; the mel spectrogram is an internal representation.

Evaluation ​

  • MOS (Mean Opinion Score). 1–5 scale, crowd-sourced. Still the gold standard; painfully slow.
  • CMOS (Comparative MOS). A-vs-B preference. Tighter confidence intervals per annotation.
  • UTMOS, DNSMOS. Reference-free neural MOS predictors. Used for leaderboards.
  • CER (Character Error Rate) via ASR. Run TTS output through Whisper, compute CER against the input text. Proxy for intelligibility.
  • SECS (Speaker Embedding Cosine Similarity). Voice-cloning quality.

2026 numbers on LibriTTS test-clean:

ModelUTMOSCER (via Whisper)Size
Ground truth4.081.2%—
F5-TTS3.952.1%335M
XTTS v23.813.5%470M
VITS3.623.1%25M
Kokoro v0.193.871.8%82M
Parler-TTS Large3.762.8%2.3B

Build It ​

Step 1: phonemize input ​

python
from phonemizer import phonemize
ph = phonemize("Hello world", language="en-us", backend="espeak")
# 'həloʊ wɜːld'

Phonemes are the universal bridge. Avoid feeding raw text to anything below VITS-level quality.

Step 2: run Kokoro (2026 CPU default) ​

python
from kokoro import KPipeline
tts = KPipeline(lang_code="a")  # "a" = American English
audio, sr = tts("Please remind me to water the plants at 6 pm.", voice="af_bella")
# audio: float32 tensor, sr=24000

Runs offline, single file, 82M params.

Step 3: run F5-TTS with voice cloning ​

python
from f5_tts.api import F5TTS
tts = F5TTS()
wav = tts.infer(
    ref_file="my_voice_5s.wav",
    ref_text="The quick brown fox jumps over the lazy dog.",
    gen_text="Please remind me to water the plants.",
)

Pass a 5-second reference clip + its transcript; F5 clones prosody and timbre.

Step 4: HiFi-GAN vocoder from scratch ​

Too big to fit in a tutorial script, but the shape is:

python
class HiFiGAN(nn.Module):
    def __init__(self, mel_channels=80, upsample_rates=[8, 8, 2, 2]):
        super().__init__()
        # 4 upsample blocks, total 256x to go from mel-rate to audio-rate
        ...
    def forward(self, mel):
        return self.blocks(mel)  # -> waveform

Training: adversarial (discriminator on short windows) + mel-spectrogram reconstruction loss + feature-matching loss. Commoditized — use pretrained checkpoints from hifi-gan repo or nvidia-NeMo.

Step 5: the full pipeline (pseudocode) ​

python
text = "Please remind me at 6 pm."
phones = phonemize(text)
mel = acoustic_model(phones, speaker=alice)      # [T, 80]
wav = vocoder(mel)                                # [T * 256]
soundfile.write("out.wav", wav, 24000)

Use It ​

The 2026 stack:

SituationPick
Real-time English voice assistantKokoro (CPU) or XTTS v2 (GPU)
Voice cloning from 5 s referenceF5-TTS
Commercial character voicesElevenLabs v2.5
Audiobook narrationElevenLabs v2.5 or XTTS v2 + fine-tune
Low-resource languageTrain VITS on 5–20 h target-lang data
Expressive / emotion tagsElevenLabs v2.5 or StyleTTS 2 fine-tune

Open-source leader as of 2026: F5-TTS for quality, Kokoro for efficiency. Don't reach for Tacotron unless you are a historian.

Pitfalls ​

  • No text normalizer. "Dr. Smith" reads as "Doctor" or "Drive"? "2026" as "twenty twenty six" or "two zero two six"? Normalize BEFORE phonemizer.
  • OOV proper nouns. "Ghumare" → "ghyu-mair"? Ship a fallback grapheme-to-phoneme model for unknown tokens.
  • Clipping. Vocoder output rarely clips, but mel scaling mismatch at inference can overshoot ±1.0. Always np.clip(wav, -1, 1).
  • Sample-rate mismatch. Kokoro outputs 24 kHz; your downstream pipeline expects 16 kHz → resample or get aliasing.

Ship It ​

Save as outputs/skill-tts-designer.md. Design a TTS pipeline for a given voice, latency, and language target.

Exercises ​

  1. Easy. Run code/main.py. Builds a phoneme dictionary from a toy vocab, estimates duration per phoneme, and prints a fake "mel" schedule.
  2. Medium. Install Kokoro, synthesize the same sentence at voice af_bella and am_adam. Compare audio durations and subjective quality.
  3. Hard. Record a 5-second reference clip of yourself. Use F5-TTS to clone it. Report SECS between reference and cloned output.

Key Terms ​

TermWhat people sayWhat it actually means
PhonemeSound unitAbstract sound class; 39 in English (ARPABet).
Duration predictorHow long each phoneme lastsNon-AR model output; integer frames per phoneme.
VocoderMel → waveformNeural net mapping mel-spec to raw samples.
HiFi-GANStandard vocoderGAN-based; dominant 2020–2024.
MOSSubjective quality1–5 mean opinion score from human raters.
SECSVoice-clone metricCosine similarity between target and output speaker embedding.
F5-TTS2024 open-source SOTAFlow-matching diffusion; zero-shot cloning.
KokoroCPU English leader82M-param model, Apache 2.0.

Further Reading ​