Human-1: A Full-Duplex Conversational Model for Hindi

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Human-1 by Josh Talks is the first full-duplex spoken dialogue model for Hindi, built by adapting Kyutai's Moshi architecture. It enables real-time, natural Hindi conversation with support for interruptions, overlaps, backchannels, and natural turn-taking — trained on 26,000 hours of real spontaneous Hindi conversations from 14,695 speakers.

Model Details

What was changed from base Moshi

The original English SentencePiece tokenizer was replaced with a Hindi SentencePiece model (32,000 vocabulary) trained on a large Hindi text corpus. This required reinitialisation of three vocabulary-dependent parameter groups:

• text_emb — text token embedding in the Temporal Transformer
• depformer.emb.0 — text token embedding in the Depth Transformer
• text_linear — text output projection layer

All audio processing components (Mimi codec) and remaining transformer weights retain their pre-trained values. Mimi generalises to Hindi without retraining (STOI: 0.878, PESQ: 2.55).

For full architecture details, see the Moshi paper.

Training

Data

The model was trained on a purpose-built corpus of 26,000 hours of real Hindi spontaneous conversations — to our knowledge, the largest conversational speech corpus for any Indian language.

The stereo recording format with separate speaker channels enables direct learning of turn-taking, overlaps, and backchannels from natural interactions — without requiring artificial speaker diarisation.

Two-stage training recipe

Stage 1 — Pre-training on the full 26,000-hour corpus. Learning rate of 3×10⁻⁵ (matching original Moshi pre-training). AdamW with β₁=0.9, β₂=0.95, weight decay 0.1. Effective batch size of 64 (~2.9 hours of audio per update). Trained for 1 epoch (~10,000 steps) in approximately 13 hours on 8× NVIDIA H100 80GB GPUs.

Stage 2 — Fine-tuning on ~990 hours of curated high-quality conversational data. Split learning rates: 2×10⁻⁶ for the Temporal Transformer, 4×10⁻⁶ for the Depth Transformer. Optimal checkpoint selected at step 4,812 based on minimum total validation loss (3.370).

Training infrastructure

8× NVIDIA H100 80GB GPUs with bf16 mixed precision.

Evaluation

Perplexity

Measured using Sarvam-1 (2B) on Whisper-v3 transcriptions of generated speech.

Human Evaluation

130 evaluators completed 2,125 rating tasks comparing human speech with model responses. Each instance contained two audio samples (Voice A: Human, Voice B: Model) rated on 5-point Likert scales for naturalness and clarity.

Perceptual quality:

Generated speech achieves high perceptual quality, with naturalness scores approaching human speech and most pairwise comparisons resulting in ties.

Conversational rubric evaluation:

Evaluators also assessed conversational quality using three binary rubric questions measuring whether generated responses behave like natural conversational speech.

While the model frequently produces speech that sounds human-like, maintaining contextual relevance and producing fully complete conversational responses remains an ongoing challenge.

Turn-Taking Analysis

Temperature τ=0.9 produces turn-taking dynamics closest to ground-truth.

Conversation Style

Human-1 is trained on topic-driven conversations - real dialogues where two speakers discuss a subject naturally, with backchannels, interruptions, and organic turn-taking.

After an initial introduction, the model will typically propose a topic and steer the conversation toward it, preferring structured discussion over open-ended chitchat. Users can also introduce their own topic - the model will pick it up and engage in a focused discussion around it. This is an intentional design choice - the training data consists of real conversations where speakers engage in focused, in-depth discussions on assigned topics.

This makes the model particularly well-suited for domain-specific conversational applications. Our key finding is that the model's ability to stay on-topic emerges naturally from the structure of the training data alone - without any explicit prompting, reward shaping, or guardrails. This suggests that with sufficient hours of domain-specific conversational data, this approach can produce models that learn the conversational norms of virtually any domain - customer support, healthcare consultations, language tutoring, sales, therapy, and more - opening a direct path from curated conversations to deployable, real-world voice agents. Exploring this is an active direction of our future work.

Files

├── model.safetensors                              # Human-1 LM weights
├── tokenizer-e351c8d8-checkpoint125.safetensors   # Mimi audio codec (frozen, from Moshi)
├── tokenizer_hindi.model                          # Hindi SentencePiece tokenizer
├── tokenizer_hindi.vocab                          # Vocabulary reference
├── hindi_moshi_architecture.svg                   # Architecture diagram
└── README.md

Quick Start

1. Install uv

curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env

2. Create project and install dependencies

uv init human-1 && cd human-1
uv python install 3.12
uv python pin 3.12
uv add moshi huggingface_hub

3. Download the model

uv run huggingface-cli download JoshTalksAI/Human-1 --local-dir ./weights

4. Run the server

uv run -m moshi.server \
    --moshi-weight ./weights/model.safetensors \
    --mimi-weight ./weights/tokenizer-e351c8d8-checkpoint125.safetensors \
    --tokenizer ./weights/tokenizer_hindi.model

Intended Use

The model is intended for research in full-duplex spoken dialogue systems for Hindi and Indian languages. It can be used as a conversational agent for casual Hindi conversations.

Limitations

• Trained primarily on Hindi conversational speech. Performance on other languages or domains is not guaranteed.
• Inherits limitations from the base Moshi architecture regarding audio quality at 1.1 kbps bitrate.
• Hindi text tokens are sparser relative to audio (~75% PAD ratio vs. 65% in English) due to Devanagari encoding more phonemic content per token.
• Not intended for impersonation or any malicious use.
• This model is for research purposes. We do not recommend it for providing advice or performing any professional duty.

Citation

@article{singh2026human1,
  title   = {Human-1 by Josh Talks : A Full-Duplex Conversational Modeling Framework in Hindi using Real-World Conversations},
  author  = {Bhaskar Singh and Shobhit Banga and Pranav Sharma},
  year    = {2026},
  institution = {JoshTalks}
}

Acknowledgments

Built on Moshi by Kyutai. We thank the 14,695 speakers who contributed to the Hindi conversational corpus.