GigaChat Audio: Time-Aware Large Audio Language Model
Aleksandr Kutsakov, Mariia Sadovina, Georgii Gospodinov, Alexandr Maximenko, Oleg Kutuzov, Pavel Bogomolov, Fyodor Minkin
GigaChat Audio uses periodic temporal anchors to enable precise event grounding and summarization in audio up to 120 minutes long.
How can we enable large audio language models to perform precise temporal grounding over long-form (up to 120-minute) audio recordings?
Audio-conditioned Large Language Models (LLMs) struggle to ground events in long recordings, often producing non-parseable or coarse timestamps when asked to locate specific moments in hour-long files. The authors introduce GigaChat Audio, which interleaves periodic "inter-timing" markers directly into the audio token stream to act as temporal anchors. This approach maintains stable grounding accuracy across 120-minute inputs, whereas standard models collapse as audio duration increases.
Paper Primer
The core mechanism hinges on inserting explicit time markers (e.g., "hh:mm:ss") into the continuous audio token stream at regular intervals. These markers act as a "temporal grid": they force the model to align its internal representation with absolute time, preventing the drift that occurs when a model must track duration solely through latent audio tokens.
Periodic temporal anchors are necessary for long-form grounding stability.
Removing inter-timings causes long-form temporal grounding accuracy (mIoU) to collapse from 53.8 to 14.2 on 20–40 minute audio segments. A 73% relative performance drop in grounding precision when anchors are removed.
Training on a mixture of audio durations is required for length generalization.
Models trained only on short audio fail to extrapolate to long recordings, while models trained only on long audio suffer performance degradation on short clips. The model trained on a full duration mixture outperforms single-regime models across all tested audio lengths.
Why is this approach better than simply asking an LLM to output a timestamp?
Standard models lack a "verifiability primitive" for time; they often hallucinate timestamps or produce coarse references because time is not naturally represented in standard audio token streams. By interleaving markers, the model is forced to anchor its generation to a specific temporal coordinate during the input phase.
What is the trade-off between anchor frequency and model performance?
More frequent anchors improve precision (e.g., 7-second intervals yield 1.5s median error vs. 3s for 60-second intervals) but increase the token overhead. The authors find that even sparse anchors (once per minute) are sufficient for reliable long-form grounding.
For researchers building long-form audio systems, temporal grounding is not a post-processing task but an input-level requirement; inserting periodic anchors is the most robust way to ensure verifiable, time-aligned outputs.
Motivation and Problem Framing
Audio‑conditioned LLMs struggle to anchor events in long recordings, hindering reliable temporal grounding.
Long‑form audio (meetings, podcasts, lectures) demands more than content extraction; users need to know *when* events occur. Existing audio‑conditioned LLMs treat time as an afterthought, producing vague or malformed timestamps that break downstream navigation.
Temporal grounding is the ability to map a model’s answer to an exact time interval in the source audio, so a user can jump directly to the supporting segment.
How does Temporal Grounding differ from simply outputting a rough timestamp?
Rough timestamps give a single point estimate (e.g., “around 3 min”), which may be ambiguous or unparsable. Temporal grounding requires a precise interval (or exact start/end) that can be programmatically aligned with the audio waveform, and it is evaluated with strict overlap metrics (mIoU) rather than informal guesses.
The core challenge is achieving reliable temporal awareness in long‑form audio, which demands explicit timing anchors and length‑aware training.
The GigaChat Audio Architecture
Method details the interleaved tokenization, timing tokens, and synthetic supervision pipeline that enable temporal grounding.
Long audio recordings cause LLMs to drift temporally, so the model must be anchored to explicit timestamps.
The input stream we feed the LLM alternates text tokens, audio tokens, and timing tokens, so the model sees time information woven directly into its context.
How does Interleaved Tokenization differ from simply concatenating all modalities?
Concatenation would place all tokens in a single block without any marker of when audio or time tokens occur, forcing the model to infer boundaries from content alone. Interleaving inserts explicit timing tokens that act as anchors, letting attention directly link events to their timestamps.
Start with text: T1, T2.
Insert timing token “00:00:02”.
Continue with text T3, then audio A1.
Insert timing token “00:00:04”.
Finish with audio A2.
The model can directly attend from a word to the nearest timing token, preserving temporal order without extra processing.
Special tokens that encode a concrete timestamp (e.g., hh:mm:ss) are placed into the token stream, giving the model a precise temporal reference point.
Why not let the model output raw timestamps instead of using dedicated timing tokens?
Generating raw timestamps requires the model to produce numeric strings and then interpret them, which is error‑prone and breaks the token‑level training regime. Timing tokens keep everything in the discrete token space, leveraging the same learned embeddings and attention patterns.
After processing the first minute of audio, insert timing token “00:01:00”.
Continue processing the second minute.
At the end of the clip, insert final timing token “00:02:00”.
The regular spacing gives the model a rhythmic scaffold, making it easier to learn the mapping from content to time.
We generate training data by aligning transcripts to audio, creating question‑answer pairs, and filtering them with a verifier to ensure temporal consistency.
How does the verifier differ from a simple heuristic filter?
A heuristic might drop any pair with mismatched timestamps, but the verifier recomputes the interval from the full transcript and measures overlap, preserving pairs that are temporally coherent even if the raw timestamps differ slightly.
Run an ASR model with time‑aligning to obtain a timestamped transcript.
Feed the transcript to a Q&A generator that samples ~10‑minute fragments and produces question‑answer pairs.
Pass each pair to a verifier that checks interval overlap against the full transcript.
Keep only verified pairs and split them into train (90 %) and eval (10 %) at the audio level.
For eval, generate five answer variants per question with an answer‑only generator.
Aggregate the variants and filter by median overlap to produce the final evaluation set.
The ASR+aligner outputs timestamps for each word.
The Q&A generator selects the fragment “welcome” and creates the pair (Q: “When does the greeting occur?”, A: “At 00:10”).
The verifier checks that the answer’s interval overlaps the transcript interval for “welcome” and accepts it.
The pair is added to the training set.
Verification guarantees that generated QA pairs respect the true temporal structure, providing reliable supervision without manual labeling.
**Figure 1.** Architecture of a time-aware audio LLM. Token streams are interleaved: text tokens, continuous audio tokens, timing tokens (either text-form hh:mm:ss or special ones).
**Figure 2.** Synthetic supervision pipeline for temporal grounding. Audio is transcribed and aligned to obtain a timestamped transcript. For QA-style data, we sample a ~10-minute transcript fragment and generate (Q, A) pairs; a verifier checks each pair against the full timestamped transcript to enforce global consistency. We split train/eval at the audio level (all questions from the same recording are in the same split). For eval, we sample 5 answer variants for each question with an answer-only generator, then aggregate and filter.
Together, these mechanisms give the model explicit temporal anchors while preserving the flexibility of a standard LLM.
Empirical Evaluation and Ablations
Inter‑timings dramatically improve long‑form audio grounding and summarization.
Our model with 7 s inter‑timings attains 65.2 $mIoU$ on long‑form temporal grounding, out‑performing the next best open‑source baseline by 12.0 $mIoU$.
Table 1 shows 65.2 $mIoU$ for our 7 s variant versus 53.8 $mIoU$ without inter‑timings and 41.4 $mIoU$ for the strongest open‑source competitor.
**Figure 3.** *Length extrapolation up to 120 min for temporal grounding in mIoU (↑).* Rows are training duration windows, columns are eval audio lengths. Shading is column-normalized, indicating relative quality within each eval duration.
AudioGrounding asks the model to locate an acoustic event within a short clip by predicting its start‑end interval.
AMI supplies multi‑speaker meeting recordings with word‑level timestamps, enabling phrase‑level temporal queries.
DAQA is a time‑aware audio QA set where each question asks for the start time of an event.
Inter‑timings deliver large performance gains on long‑form benchmarks.
Questions & answers
What is the main contribution of GigaChat Audio?
GigaChat Audio introduces a mechanism called 'inter-timing' markers — explicit time tokens (e.g., 'hh:mm:ss') interleaved at regular intervals into the audio token stream — that act as temporal anchors, enabling reliable temporal grounding in recordings up to 120 minutes long.
What problem does GigaChat Audio address?
It addresses the failure of standard audio-conditioned LLMs to accurately locate specific events in long recordings; existing models hallucinate timestamps or produce vague, non-parseable time references because time is not naturally represented in standard audio token streams.
Why do standard audio-conditioned LLMs struggle with temporal grounding?
Standard models lack a 'verifiability primitive' for time, meaning they must infer duration solely through latent audio tokens, which causes temporal drift and leads to hallucinated or coarse timestamps as audio length increases.
How does GigaChat Audio's inter-timing mechanism work?
The model inserts explicit time markers at regular intervals directly into the continuous audio token stream, creating a 'temporal grid' that forces the model to align its internal representations with absolute time coordinates during the input phase.
How does interleaved tokenization differ from concatenating all modalities?
Concatenation places all tokens in a single block without markers indicating when audio or time tokens occur, forcing the model to infer boundaries from content alone, whereas interleaving inserts explicit timing tokens that let attention directly link events to their timestamps.
Why does GigaChat Audio use dedicated timing tokens rather than having the model output raw timestamp strings?
Generating raw timestamps requires the model to produce numeric strings and then interpret them, which is error-prone and disrupts the token-level training regime; timing tokens keep everything in the discrete token space, leveraging the same learned embeddings and attention patterns.
What is the trade-off between anchor frequency and model performance?
More frequent anchors improve precision — for example, 7-second intervals yield a 1.5-second median error versus 3 seconds for 60-second intervals — but increase token overhead; the authors find that even sparse anchors (once per minute) are sufficient for reliable long-form grounding.
How does temporal grounding differ from simply outputting a rough timestamp?
Temporal grounding requires a precise interval (start and end time) that can be programmatically aligned with the audio waveform and is evaluated with strict overlap metrics such as mIoU, whereas a rough timestamp is a single coarse point estimate that may be ambiguous or unparsable.
What evaluation metric does the paper use for temporal grounding?
The paper uses mean Intersection over Union (mIoU) as the primary evaluation metric for temporal grounding, measuring how well predicted time intervals overlap with ground-truth intervals.
How does GigaChat Audio perform on long-form audio compared to standard models?
GigaChat Audio maintains stable grounding accuracy across 120-minute inputs, whereas standard models collapse in accuracy as audio duration increases; the paper reports that inter-timings deliver large performance gains on long-form benchmarks.
What role does the verifier play in GigaChat Audio's pipeline?
The verifier recomputes time intervals from the full transcript and measures overlap, preserving training pairs that are temporally coherent even if raw timestamps differ slightly — unlike a simple heuristic filter that would discard any pair with mismatched timestamps.
What types of audio content motivate the need for GigaChat Audio?
The paper is motivated by long-form audio such as meetings, podcasts, and lectures, where users need to know not just what was said but precisely when events occurred for downstream navigation and retrieval.
What are the limitations or open questions acknowledged by the paper?
The paper does not explicitly enumerate a dedicated limitations section in the provided content; however, it acknowledges a token-overhead trade-off with denser anchors and notes that even sparse anchors (once per minute) suffice, implying that optimal anchor frequency remains a design choice dependent on use case.
How does GigaChat Audio differ from prior audio-conditioned LLM approaches?
Prior audio-conditioned LLMs treat time as an afterthought and rely on the model to infer temporal position from latent audio tokens alone, whereas GigaChat Audio explicitly encodes time into the input token stream via periodic inter-timing markers, making temporal grounding an input-level requirement rather than a post-processing task.
What is the practical implication for researchers building long-form audio systems?
The paper concludes that temporal grounding is not a post-processing task but an input-level requirement, and that inserting periodic timing anchors into the token stream is the most robust way to ensure verifiable, time-aligned outputs.
Who authored GigaChat Audio and where was it published?
The paper does not specify individual author names or the publication venue in the provided content; it is available on arXiv at arxiv.org/abs/2607.10387.
Key terms
- inter-timing markers
- Explicit time tokens (formatted as 'hh:mm:ss') inserted at regular intervals into the audio token stream to serve as temporal anchors for the language model.
- temporal grounding
- The task of precisely identifying the start and end time of a specific event within an audio recording, evaluated by how well predicted intervals overlap with ground-truth intervals.
- audio-conditioned LLM
- A large language model that accepts audio input (in addition to text) and generates text responses based on the audio content.
- interleaved tokenization
- A method of combining multiple modalities (e.g., audio tokens and time tokens) by alternating them in a single sequence rather than concatenating them as separate blocks.
- mIoU (mean Intersection over Union)
- An evaluation metric that measures the average overlap between predicted time intervals and ground-truth time intervals, used here to assess temporal grounding accuracy.
- temporal drift
- The progressive loss of accurate time-position tracking that occurs in a model when it must infer elapsed duration solely from latent audio representations without explicit time anchors.
- verifiability primitive
- A built-in mechanism that allows a model to produce outputs that can be checked against an objective ground truth, here referring to explicit time tokens that make timestamp predictions verifiable.
- verifier
- A component in the GigaChat Audio pipeline that recomputes time intervals from full transcripts and measures temporal overlap to filter training data for coherence.
- temporal grid
- The structured sequence of evenly spaced time markers inserted into the token stream that collectively define a reference frame for absolute time within an audio recording.
- long-form audio
- Audio recordings of extended duration (e.g., meetings, podcasts, lectures) that can span tens of minutes to several hours, posing unique challenges for temporal understanding.
- token overhead
- The additional tokens consumed by inserting timing markers into the sequence, which increases computational cost as anchor frequency increases.