Karobben

0 Posted 1969-12-31Updated 2025-09-2319 minutes read (About 2822 words)

I’m interested in voice-to-text. Is there a good AI model to do it? I want to work on a new language but have no resource to train it from the beginning. So, fine-tuning would be my first choice

Yes — there are good AI / ML models and toolkits today that let you do voice-to-text (automatic speech recognition, ASR) and fine-tune them (rather than training from scratch). Given your constraint (a new language, limited resource), fine-tuning or transfer learning is indeed usually the right path. Below is a breakdown of promising models, factors to watch out for, and a possible workflow.

What makes a “good” model for fine-tuning ASR (voice → text)

Here are the key attributes you’ll want in your base model:

Multilingual / broad coverage: A model pretrained on many languages and acoustic conditions gives you a better starting point, even if your target language is under-represented.
Open / accessible: You want architectures, code, weights, and fine-tuning recipes you can use.
Encoder-decoder / sequence-to-sequence or “end-to-end”: Modern ASR architectures (e.g. using Transformers, Conformers) are easier to fine-tune end-to-end.
Reasonable size / modifiability: Extremely large models are expensive to fine-tune. Being able to “freeze” parts of the model helps.
Good community / tooling: Hugging Face, ESPnet, etc., make it easier to adapt, debug, and deploy.

Promising models / toolkits

Here are some of the leading candidates that people currently use or experiment with:

Model / Toolkit	Strengths / Use Cases	Considerations / Challenges
OpenAI Whisper	It’s multilingual (supports ~96 languages) and is open source. Hugging Face has published a guide for fine-tuning it on multilingual ASR datasets. (Hugging Face)	Whisper was not originally designed for custom-language fine-tuning, so if your language is very distant or has unique phonetics, performance gains may be limited. Also, memory / GPU needs for fine-tuning can be high.
Wav2Vec 2.0 / variants	Self-supervised speech models (Facebook / Meta) that learn good acoustic representations; you can fine-tune a head for transcription. Many ASR systems use them. (Medium)	You’ll need a good amount of supervised paired audio-text data for the final layers. If your language doesn’t appear in pretraining, the lower layers may not generalize well.
ESPnet / ESPnet-EZ	A well-regarded speech toolkit that supports fine-tuning, training, and inference, with recipes for many languages. The “EZ” variant aims to simplify usage. (arXiv)	There is (as always) a non-trivial engineering burden; data preprocessing, alignment, and handling of edge cases (noise, speaker variation) can get tricky.
Fairseq S2T	From Facebook’s fairseq, they support end-to-end speech-to-text modeling and have recipes for transfer learning. (arXiv)	You’ll need to integrate preprocessing pipelines (spectrograms, tokenizers) and possibly leverage language models to boost transcription accuracy.
FunASR	A toolkit that supports ASR, VAD, punctuation, etc. It has models and scripts set up for fine-tuning. (GitHub)	As always, performance depends heavily on data, and deployment for production can require additional tuning.
Universal Speech Model (USM)	A newer large-scale model that claims support for >100 languages; the approach uses large unlabeled pretraining then fine-tuning. (arXiv)	Model size and compute needs might be high; licensing / openness is something to check.

Given your situation (a new language + limited resources), Whisper is a very attractive first choice — the community is active, and you can piggyback off its multilingual strengths while fine-tuning to your language. The Hugging Face “Fine-tune Whisper” guide is a good starting point. (Hugging Face)

What you need (data and infrastructure)

To fine-tune a speech-to-text model well, here are the core requirements:

Paired audio-text data
You’ll need audio samples in your target language plus corresponding transcriptions. The amount of data needed depends on how much transfer is possible from the base model, but even a few hours can help.
- For low-resource languages, sometimes people bootstrap from small corpora, crowd-sourcing, or reusing community speech data (if any).
- You need good audio quality and alignment (i.e. transcriptions aligned to audio segments).
Preprocessing / feature extraction
- Convert raw audio (e.g. WAV) to features the model expects (e.g. log-Mel spectrograms).
- Resample or normalize audio so it matches what the pretrained model expects.
- Tokenization / vocabulary: in many cases you’ll reuse the pretrained model’s tokenizer (so you don’t have to build one from scratch) — e.g. Whisper’s tokenizer. (Hugging Face)
- Possibly filtering / cleaning your data (silence trimming, noise removal, normalization).
Model fine-tuning setup
- Which layers to freeze vs which to train (a common strategy is to freeze many lower layers and train/adjust higher layers).
- Learning rate schedules, regularization, gradient clipping, batch size, etc.
- Validation / evaluation (e.g. word error rate, WER) to monitor progress.
- Possibly augmenting data (speed perturbation, noise addition) to improve robustness.
Compute / GPU
- Fine-tuning even a moderately sized ASR model can require GPUs with decent VRAM (e.g. ≥16 GB).
- You’ll benefit from mixed precision / gradient checkpointing to reduce memory.
- For lower resource languages, you might start with a smaller model (e.g. Whisper “small” or “base”) before scaling.
Post-processing / Language model / Decoding
- Often the raw model transcription can be improved by integrating a language model (LM) that can help fix errors, especially for words that are linguistically plausible.
- Sometimes you use beam search decoding, or rescoring with an external LM.
- Handling punctuation, casing, special tokens, etc.

Draft workflow (for your new language)

Here’s a sketch of how you might proceed:

Select a base model
Start with Whisper (small or base) or another multilingual ASR model with open weights.
Collect / prepare your dataset
- Gather as many high-quality audio-transcription pairs as possible in your target language.
- Partition into training / validation / test sets.
- Clean and align transcriptions.
Preprocess the data
- Resample to expected sampling rate.
- Extract features (log-Mel spectrograms, etc.).
- Tokenize the text using the base model’s tokenizer (if compatible).
Fine-tuning
- Freeze some layers initially, train a subset of layers, then unfreeze more gradually.
- Use a small learning rate, warmup, decay schedule.
- Monitor validation error (WER) and overfitting.
- Optionally use data augmentation (noise, speed, volume changes) to improve robustness.
Decoding + Language Modeling
- Use beam search decoding.
- Optionally incorporate a language model (n-gram or neural) in rescoring to reduce errors.
- Post-process output (punctuation, casing, token cleanup).
Iterate / refine
- Identify common error types (e.g. certain phonemes, confusions) and add focused data to correct them.
- Expand the dataset over time.
- Possibly fine-tune or adapt a small language model from monolingual text in your language to help LM rescoring.
Deployment / inference
- Export the fine-tuned model.
- Optimize for inference (quantization, pruning, streaming).
- Test in real-world audio conditions (noise, accents, etc.).

Potential challenges & caveats

Data scarcity: If your language is very low-resource, you may find it hard to get good quality paired audio-text. The model may struggle with phonemes or acoustic phenomena not seen in pretraining.
Phonetic divergence: If your language has sounds or phoneme inventories far from those in the pretrained model’s languages, transfer may be weak.
Overfitting: With small data, overfitting is a risk. Use strong validation, regularization, and freezing strategies.
Inference robustness: Real-world audio has noise, reverberation, speaker variation. The fine-tuned model must generalize beyond the training set. Using augmentation during training helps.
Decoder / language modeling: The output may have mistakes in grammar, rare words, or ambiguous segments; having a robust LM rescoring helps.
Licensing / openness: Check the licenses of base models (some models have constraints).
Compute costs: Fine-tuning even a “small” ASR model can be expensive. Careful resource planning is needed.

If you like, I can propose a concrete fine-tuning recipe for Whisper (or another model) tailored for your target language (given assumptions about how much data you have), or help you pick the best model for your situation. Do you want me to outline that next?

I want to working on a dialog of mandering. Which model would can by my first choice?

For Mandarin conversational (dialog) speech-to-text, the best “first choice” today is OpenAI Whisper, for three main reasons:

✅ Why Whisper is a strong starting point

Excellent Mandarin accuracy out-of-the-box
- Trained on a huge multilingual dataset that already includes a lot of Mandarin, both Mainland and Taiwan accents.
- Handles code-switching with English words (common in casual dialog).
Open and easy to fine-tune
- Weights and training scripts are public.
- Hugging Face and other communities provide ready-made fine-tuning recipes.
Robust to real-world dialog conditions
- Works well on overlapping speakers, background noise, and variable speaking speed.

Fine-tuning strategy for Mandarin dialog

Start with a mid-size checkpoint (e.g. whisper-small or whisper-medium).
Collect a few dozen to a few hundred hours of paired conversational Mandarin audio + transcripts (telephone, podcast, meeting recordings, etc.).
Fine-tune only the final layers or use LoRA/PEFT to keep GPU needs modest.
Use character-level or BPE tokenizer that Whisper already provides—no need to build your own.

Strong alternatives (if you want to compare)

Model	Why consider it	Notes
Wav2Vec 2.0 (CN-pretrained)	Multiple Chinese checkpoints on Hugging Face; very good for clean speech.	Needs a separate decoder or CTC head; slightly more engineering for dialogs with heavy overlap.
FunASR / Paraformer	Designed in China for Mandarin ASR; supports real-time streaming and diarization.	Great for production pipelines, but community/fine-tune docs are mostly Chinese.
ESPnet Mandarin recipes	Flexible research toolkit with many Mandarin corpora recipes (AISHELL, HKUST, etc.).	More code to manage, but high accuracy if you want a fully custom system.

Recommended first step

If your goal is quickest path to a good Mandarin dialog ASR:

Start with Whisper-medium (or large-v2 if you have GPU budget).
Prepare a small conversational dataset (even 10–20 h helps).
Fine-tune using Hugging Face’s transformers + PEFT/LoRA.

This will give you strong baseline performance and a clean path to scale if you gather more Mandarin dialog data later.

Here is an idea: the pronunciation of Chinese is constantly changing because of the official language. With the great impact of the mandary, dialogues are changing, too. If I want to use the AI to study how this happened based on the current data, can I even predict the old pronunciation from ancient? Currently, we have extensive data on how dialog changes; for ancient times, we have partial written records of pronunciation. If this task is good for AI, how should I make the blueprint to achieve it? Please give me some idea or clear steps for the plan would be better.

Here’s a blueprint for using AI to study how Mandarin pronunciation and dialog have evolved and to approximate ancient pronunciations:

1. Define Your Research Goals

Modern change: Model how Mandarin pronunciations shift over decades (regional accent leveling, loanword effects, etc.).
Historical inference: Predict older pronunciations (e.g., Middle Chinese) from partial written/phonetic records.

2. Data Collection

Contemporary data:
- Large corpora of modern spoken Mandarin (dialogues, TV, social media voice data) with time stamps.
- Regional dialect recordings (Beijing, Sichuan, Taiwanese, etc.) to capture variation.
Historical data:
- Rhyme dictionaries (e.g., 《广韵》), ancient poetry with known rhymes, reconstructed Middle Chinese readings.
- Early romanizations and missionary records.
Metadata:
- Year, region, speaker info for modern recordings.
- Historical context (dynasty, region, script type) for ancient records.

3. Modeling the Change

Diachronic speech modeling
- Train time-aware acoustic embeddings (e.g., Wav2Vec 2.0 or HuBERT) with year/region as conditioning labels to map sound shifts over time.
Graph or sequence evolution models
- Use graph neural networks or temporal transformers to learn sound change trajectories.
Text-to-phoneme (G2P) + language evolution
- Build G2P models for different historical stages, aligning ancient texts with modern pronunciations.

4. Ancient Pronunciation Prediction

Input:
- Middle Chinese reconstructions, rhyme patterns, historical scripts.
Approach:
- Sequence-to-sequence models to translate modern phonemes into reconstructed historical phonemes.
- Probabilistic models (Bayesian phylogenetics or neural diffusion) to infer ancestral sounds.

5. Evaluation

Cross-validate with existing reconstructions by historical linguists.
Test on known sound shifts (e.g., entering tone loss, palatalization).

6. Practical Steps

Data pipeline: Build a database combining modern speech + historical text, standardized phonetic transcription (IPA).
Preprocessing: Normalize and time-align speech. Convert text to phoneme sequences.
Model training:
- Stage 1: Train a modern Mandarin ASR (Whisper, Wav2Vec2) to get accurate phoneme-level outputs.
- Stage 2: Build a time-conditioned model to detect and predict pronunciation drift.
- Stage 3: Train historical inference models with rhyme dictionaries and reconstructed data.
Visualization: Interactive timeline or “sound-change map” showing how initials/finals evolve.

7. Challenges

Sparse historical audio (none exists)—must rely on textual phonology.
Need expert linguistic annotation for ground truth.
Regional diversity: Mandarin itself is a moving target.

Summary:
This project is feasible as an AI + historical linguistics collaboration. Start with a modern diachronic speech model, then integrate historical phonology to back-predict older sounds. The key is a carefully curated dataset and time-aware modeling of pronunciation shifts.

Please give me more details about the ‘time-conditioned model’, I really need some idea and help in this part

Here’s a deeper look at what a time-conditioned model could be and how you might design it for Mandarin pronunciation evolution.

1. Core Idea

A time-conditioned model learns a mapping from speech/text features to phonetic representations while explicitly conditioning on a time variable (e.g., year of recording, dynasty for historical text).
Intuition: the model captures not only spatial variation (dialects) but also temporal drift in phoneme realizations.

2. Data Encoding

Inputs:
- Acoustic features (e.g., log-Mel spectrograms) or phoneme sequences from ASR.
- Time stamp as a continuous variable (e.g., year normalized to [0,1]) or as a categorical embedding (e.g., dynasty code).
- Optional: geographic embeddings (region/province).
Outputs:
- Modern phonemes (for prediction) or historical phonemes (for reconstruction).

3. Architectural Options

a. Time-Conditioned Variational Autoencoder (VAE)

Encoder maps speech → latent phoneme space.
Decoder takes latent + time embedding → reconstructed phoneme distribution.
The latent space becomes a “phonetic continuum” you can query at any time slice.

b. Temporal Transformer

Use a Transformer (like Whisper or Wav2Vec2) but add:
- Time embeddings added to each layer (like positional encodings).
- Time can be continuous (learned sinusoidal) or discrete (year/dynasty token).
Train with objective: predict phonemes while letting the attention heads learn time-sensitive shifts.

c. Conditional Diffusion Model

Treat phoneme sequences as the “image” to be generated.
Diffusion model conditions on time embeddings to denoise from a “proto-phoneme” distribution to the target era.

d. Graph Neural Network (Dialect Graph)

Nodes: phonemes/initials/finals.
Edges: sound-change relationships.
Node features: acoustic/phonetic vectors + time.
Predict how each node’s feature changes along a temporal graph.

4. Training Strategy

Modern baseline: Train on contemporary datasets with year annotations (e.g., 1950–2025).
Historical anchoring: Add partially reconstructed Middle Chinese/Old Mandarin data with approximate “year labels.”
Loss functions:
- CTC/phoneme cross-entropy.
- Temporal regularization: encourage smooth change across adjacent time bins.
Interpolation/Extrapolation:
- Query model for phonemes at an intermediate or older time (e.g., year 1000 CE) to approximate ancient pronunciation.

5. Application to Ancient Prediction

After training, you can interpolate backwards to estimate pronunciation in centuries without audio.
Use rhyme dictionaries or historical phonology as sparse “anchors” to guide the model’s outputs.
Combine with Bayesian inference to quantify uncertainty.

6. Practical Blueprint

Data Prep: Align speech + phoneme + year/region.
Modeling: Implement a time-conditioned Transformer or VAE in PyTorch or JAX.
Evaluation:
- Compare predicted 15th-century pronunciations to reconstructions from historical linguists.
- Use metrics like KL divergence vs. expert IPA transcriptions.

This time-conditioned framework lets you treat time itself as a learnable dimension, enabling both analysis of modern shifts (regional leveling, new loanword sounds) and projection into the past to approximate ancient Mandarin pronunciation.

https://karobben.github.io/1969/12/31/test/