4.632M total params | 3.465M acoustic | 1.167M vocoder | 24 kHz | English
## Listen First These examples cover punctuation, short questions, numbers, hard words, longer phrasing, and transition-heavy prompts. | Prompt | Audio | |---|---| | "Did the timing change?" she answered. "Then why did Logan leave?" | | | Who puts a parking meter next to an ER label? | | | Please say neighborhood, statistics, and anesthesiologist clearly, without rushing through the middle syllables. | | | I said 91, not 306, which is a very different number. | | | The inference path looked natural, but the decoder still needed a smoother transition before Marcus approved the final test. | | | The appointment moved to 1:25, the invoice was $674.96, and the archive was labeled 1998. | | | If Logan sounded uneasy, then it happened near Long Beach, and the pause has to carry that. | | | The word aluminum should not steal attention from the softer ending after entrepreneur. | | ## Why This Exists Most strong TTS models are large. Many "small" TTS demos also depend on a larger external vocoder, which hides a lot of the real inference cost. Inflect-Nano-v1 counts both sides: ```text text -> acoustic model -> mel spectrogram -> neural vocoder -> waveform ``` The model is intentionally constrained: | Capability | Inflect-Nano-v1 | |---|---:| | Complete text-to-waveform stack | Yes | | External vocoder required | No | | Total inference parameters | **4.632M** | | Sample rate | **24 kHz** | | Language | English | | Voice count | 1 | | Voice cloning | No | | Multilingual | No | ## Size Context Approximate parameter comparison: | Model / class | Params | Relative to Inflect-Nano-v1 | |---|---:|---:| | Inflect-Nano-v1 | **4.63M** | 1.0x | | Kokoro-82M | ~82M | ~17.7x larger | | Chatterbox-sized TTS | ~500M | ~108x larger | | Multi-billion TTS systems | 1B+ | 216x+ larger | The comparison is about model scale, not quality parity. Larger models are expected to sound better. ## Quickstart ```bash git clone https://huggingface.co/owensong/Inflect-Nano-v1 cd Inflect-Nano-v1 pip install -r requirements.txt ``` Generate audio: ```bash python inference.py \ --text "Wait, are you actually being for real now? I can't believe it!" \ --out sample.wav ``` CPU: ```bash python inference.py \ --device cpu \ --text "Please say neighborhood, statistics, and anesthesiologist clearly." \ --out sample_cpu.wav ``` Optional controls: ```bash python inference.py \ --text "No, seriously, did Jordan leave the receipt in Albuquerque?" \ --length-scale 1.03 \ --pitch-scale 1.00 \ --energy-scale 1.00 \ --out sample_controlled.wav ``` Local Gradio demo: ```bash python app.py ``` The text frontend may download tokenizer files on first run. ## Model Summary | Component | Parameters | Notes | |---|---:|---| | Acoustic model | **3,465,125** | Compact non-autoregressive FastSpeech-style model | | Vocoder generator | **1,167,077** | Snake V2Mid HiFi-GAN-style generator | | Total inference model | **4,632,202** | Acoustic + vocoder | Model files: ```text weights/inflect_nano_v1_acoustic.pt weights/inflect_nano_v1_vocoder.pt ``` ## Architecture Inflect-Nano-v1 is a two-part TTS stack: ```text Text -> English normalization + G2P frontend -> Compact FastSpeech-style acoustic model -> 80-bin mel spectrogram -> Snake V2Mid HiFi-GAN-style vocoder -> 24 kHz waveform ``` ### Acoustic Model The acoustic model predicts duration, energy, brightness, and pitch, expands token states into frame states, and decodes 80-bin mel spectrograms. Main config: ```json { "hidden": 168, "encoder_layers": 5, "decoder_layers": 6, "decoder_ff_mult": 3, "kernel_size": 7, "speaker_dim": 64, "dropout": 0.08, "n_mels": 80, "sample_rate": 24000, "max_frames": 1400, "postnet_scale": 0.1, "use_frame_pitch": true, "abs_frame_bins": 512 } ``` Approximate acoustic split: ```text total acoustic: 3.465M encoder: 1.292M decoder: 1.211M postnet: 0.276M local context: 0.226M frame GRU: 0.128M heads/embeds/projections: remainder ``` ### Vocoder The vocoder is a custom Snake-activation HiFi-GAN-style generator. Discriminators are training-only and are not included in inference parameter counts. Main config: ```json { "variant": "snake_v2mid", "sample_rate": 24000, "n_fft": 1024, "hop_size": 256, "win_size": 1024, "num_mels": 80, "fmax": 12000.0, "upsample_rates": [8, 8, 2, 2], "upsample_kernel_sizes": [16, 16, 4, 4], "upsample_initial_channel": 144, "resblock_kernel_sizes": [3, 7, 11], "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], "activation": "snake" } ``` ## Intended Use Good fits: - Tiny local TTS experiments - Offline assistant prototypes - Embedded or low-resource speech demos - Efficient inference research - Browser/WASM-style exploration - Baseline for sub-5M TTS work Poor fits: - Production narration - Accessibility-critical output - Voice cloning - Multilingual TTS - High-fidelity studio speech - Long-form audiobook generation ## Limitations This model is intentionally tiny and has clear quality limits: - Unseen text can stumble or sound unstable. - The voice can sound robotic, buzzy, or artifacted. - Long or unusual prompts are less reliable. - The vocoder is a major quality bottleneck. - It is not a voice cloning model. - It is not multilingual. - It is not suitable for safety-critical or production accessibility use. ## Recommended Framing Use this as: > An experimental 4.63M-parameter English TTS model exploring the quality/size tradeoff for ultra-small local speech synthesis. Do not present it as SOTA or production-quality. ## License Apache-2.0. This repository includes a small third-party English text frontend derived from TinyTTS-style code for tokenization/G2P compatibility. Its license is included as `TINY_TTS_LICENSE`.