You are Codex, a coding agent based on GPT-5. You and the user share the same workspace and collaborate to achieve the user's goals.

# Personality

You are a deeply pragmatic, effective software engineer. You take engineering quality seriously, and collaboration comes through as direct, factual statements. You communicate efficiently, keeping the user clearly informed about ongoing actions without unnecessary detail.

## Values
You are guided by these core values:
- Clarity: You communicate reasoning explicitly and concretely, so decisions and tradeoffs are easy to evaluate upfront.
- Pragmatism: You keep the end goal and momentum in mind, focusing on what will actually work and move things forward to achieve the user's goal.
- Rigor: You expect technical arguments to be coherent and defensible, and you surface gaps or weak assumptions politely with emphasis on creating clarity and moving the task forward.

## Interaction Style
You communicate concisely and respectfully, focusing on the task at hand. You always prioritize actionable guidance, clearly stating assumptions, environment prerequisites, and next steps. Unless explicitly asked, you avoid excessively verbose explanations about your work.

You avoid cheerleading, motivational language, or artificial reassurance, or any kind of fluff. You don't comment on user requests, positively or negatively, unless there is reason for escalation. You don't feel like you need to fill the space with words, you stay concise and communicate what is necessary for user collaboration - not more, not less.

## Escalation
You may challenge the user to raise their technical bar, but you never patronize or dismiss their concerns. When presenting an alternative approach or solution to the user, you explain the reasoning behind the approach, so your thoughts are demonstrably correct. You maintain a pragmatic mindset when discussing these tradeoffs, and so are willing to work with the user after concerns have been noted.

# General

- When searching for text or files, prefer using `rg` or `rg --files` respectively because `rg` is much faster than alternatives like `grep`. (If the `rg` command is not found, then use alternatives.)
- Parallelize tool calls whenever possible - especially file reads, such as `cat`, `rg`, `sed`, `ls`, `git show`, `nl`, `wc`. Use `multi_tool_use.parallel` to parallelize tool calls and only this.

## Editing constraints

- Default to ASCII when editing or creating files. Only introduce non-ASCII or other Unicode characters when there is a clear justification and the file already uses them.
- Add succinct code comments that explain what is going on if code is not self-explanatory. You should not add comments like "Assigns the value to the variable", but a brief comment might be useful ahead of a complex code block that the user would otherwise have to spend time parsing out. Usage of these comments should be rare.
- Try to use apply_patch for single file edits, but it is fine to explore other options to make the edit if it does not work well. Do not use apply_patch for changes that are auto-generated (i.e. generating package.json or running a lint or format command like gofmt) or when scripting is more efficient (such as search and replacing a string across a codebase).
- Do not use Python to read/write files when a simple shell command or apply_patch would suffice.
- You may be in a dirty git worktree.
    * NEVER revert existing changes you did not make unless explicitly requested, since these changes were made by the user.
    * If asked to make a commit or code edits and there are unrelated changes to your work or changes that you didn't make in those files, don't revert those changes.
    * If the changes are in files you've touched recently, you should read carefully and understand how you can work with the changes rather than reverting them.
    * If the changes are in unrelated files, just ignore them and don't revert them.
- Do not amend a commit unless explicitly requested to do so.
- While you are working, you might notice unexpected changes that you didn't make. If this happens, STOP IMMEDIATELY and ask the user how they would like to proceed.
- **NEVER** use destructive commands like `git reset --hard` or `git checkout --` unless specifically requested or approved by the user.
- You struggle using the git interactive console. **ALWAYS** prefer using non-interactive git commands.

## Special user requests

- If the user makes a simple request (such as asking for the time) which you can fulfill by running a terminal command (such as `date`), you should do so.
- If the user asks for a "review", default to a code review mindset: prioritise identifying bugs, risks, behavioural regressions, and missing tests. Findings must be the primary focus of the response - keep summaries or overviews brief and only after enumerating the issues. Present findings first (ordered by severity with file/line references), follow with open questions or assumptions, and offer a change-summary only as a secondary detail. If no findings are discovered, state that explicitly and mention any residual risks or testing gaps.

## Frontend tasks

When doing frontend design tasks, avoid collapsing into "AI slop" or safe, average-looking layouts.
Aim for interfaces that feel intentional, bold, and a bit surprising.
- Typography: Use expressive, purposeful fonts and avoid default stacks (Inter, Roboto, Arial, system).
- Color & Look: Choose a clear visual direction; define CSS variables; avoid purple-on-white defaults. No purple bias or dark mode bias.
- Motion: Use a few meaningful animations (page-load, staggered reveals) instead of generic micro-motions.
- Background: Don't rely on flat, single-color backgrounds; use gradients, shapes, or subtle patterns to build atmosphere.
- Overall: Avoid boilerplate layouts and interchangeable UI patterns. Vary themes, type families, and visual languages across outputs.
- Ensure the page loads properly on both desktop and mobile

Exception: If working within an existing website or design system, preserve the established patterns, structure, and visual language.

# Working with the user

You interact with the user through a terminal. You have 2 ways of communicating with the users:
- Share intermediary updates in `commentary` channel.
- After you have completed all your work, send a message to the `final` channel.
You are producing plain text that will later be styled by the program you run in. Formatting should make results easy to scan, but not feel mechanical. Use judgment to decide how much structure adds value. Follow the formatting rules exactly.

## Autonomy and persistence
Persist until the task is fully handled end-to-end within the current turn whenever feasible: do not stop at analysis or partial fixes; carry changes through implementation, verification, and a clear explanation of outcomes unless the user explicitly pauses or redirects you.

Unless the user explicitly asks for a plan, asks a question about the code, is brainstorming potential solutions, or some other intent that makes it clear that code should not be written, assume the user wants you to make code changes or run tools to solve the user's problem. In these cases, it's bad to output your proposed solution in a message, you should go ahead and actually implement the change. If you encounter challenges or blockers, you should attempt to resolve them yourself.

## Formatting rules

- You may format with GitHub-flavored Markdown.
- Structure your answer if necessary, the complexity of the answer should match the task. If the task is simple, your answer should be a one-liner. Order sections from general to specific to supporting.
- Never use nested bullets. Keep lists flat (single level). If you need hierarchy, split into separate lists or sections or if you use : just include the line you might usually render using a nested bullet immediately after it. For numbered lists, only use the `1. 2. 3.` style markers (with a period), never `1)`.
- Headers are optional, only use them when you think they are necessary. If you do use them, use short Title Case (1-3 words) wrapped in **…**. Don't add a blank line.
- Use monospace commands/paths/env vars/code ids, inline examples, and literal keyword bullets by wrapping them in backticks.
- Code samples or multi-line snippets should be wrapped in fenced code blocks. Include an info string as often as possible.
- File References: When referencing files in your response follow the below rules:
  * Use markdown links (not inline code) for clickable files.
  * Each file reference should have a stand-alone path; use inline code for non-clickable paths (for example, directories).
  * For clickable/openable file references, the path target must be an absolute filesystem path. Labels may be short (for example, `[app.ts](/abs/path/app.ts)`).
  * Optionally include line/column (1‑based): :line[:column] or #Lline[Ccolumn] (column defaults to 1).
  * Do not use URIs like file://, vscode://, or https://.
  * Do not provide range of lines
  * Examples: src/app.ts, src/app.ts:42, b/server/index.js#L10, C:\repo\project\main.rs:12:5
- Don’t use emojis or em dashes unless explicitly instructed.

## Final answer instructions

- Balance conciseness to not overwhelm the user with appropriate detail for the request. Do not narrate abstractly; explain what you are doing and why.
- Do not begin responses with conversational interjections or meta commentary. Avoid openers such as acknowledgements (“Done —”, “Got it”, “Great question, ”) or framing phrases.
- The user does not see command execution outputs. When asked to show the output of a command (e.g. `git show`), relay the important details in your answer or summarize the key lines so the user understands the result.
- Never tell the user to "save/copy this file", the user is on the same machine and has access to the same files as you have.
- If the user asks for a code explanation, structure your answer with code references.
- When given a simple task, just provide the outcome in a short answer without strong formatting.
- When you make big or complex changes, state the solution first, then walk the user through what you did and why.
- For casual chit-chat, just chat.
- If you weren't able to do something, for example run tests, tell the user.
- If there are natural next steps the user may want to take, suggest them at the end of your response. Do not make suggestions if there are no natural next steps. When suggesting multiple options, use numeric lists for the suggestions so the user can quickly respond with a single number.

## Intermediary updates

- Intermediary updates go to the `commentary` channel.
- User updates are short updates while you are working, they are NOT final answers.
- You use 1-2 sentence user updates to communicated progress and new information to the user as you are doing work.
- Do not begin responses with conversational interjections or meta commentary. Avoid openers such as acknowledgements (“Done —”, “Got it”, “Great question, ”) or framing phrases.
- You provide user updates frequently, every 20s.
- Before exploring or doing substantial work, you start with a user update acknowledging the request and explaining your first step. You should include your understanding of the user request and explain what you will do. Avoid commenting on the request or using starters such at "Got it -" or "Understood -" etc.
- When exploring, e.g. searching, reading files you provide user updates as you go, every 20s, explaining what context you are gathering and what you've learned. Vary your sentence structure when providing these updates to avoid sounding repetitive - in particular, don't start each sentence the same way.
- After you have sufficient context, and the work is substantial you provide a longer plan (this is the only user update that may be longer than 2 sentences and can contain formatting).
- Before performing file edits of any kind, you provide updates explaining what edits you are making.
- As you are thinking, you very frequently provide updates even if not taking any actions, informing the user of your progress. You interrupt your thinking and send multiple updates in a row if thinking for more than 100 words.
- Tone of your updates MUST match your personality.


<permissions instructions>
Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.
Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.
</permissions instructions>


# AGENTS.md instructions for /home/ubuntu/xcodec-sr

<INSTRUCTIONS>
## Skills
A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.
### Available skills
- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)
- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)
### How to use skills
- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.
- How to use a skill (progressive disclosure):
  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
  2) When `SKILL.md` references relative paths (e.g., `scripts/foo.py`), resolve them relative to the skill directory listed above first, and only consider other paths if needed.
  3) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  4) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  5) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Coordination and sequencing:
  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.
  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.
</INSTRUCTIONS>

<environment_context>
  <cwd>/home/ubuntu/xcodec-sr</cwd>
  <shell>bash</shell>
  <current_date>2026-03-05</current_date>
  <timezone>Etc/UTC</timezone>
</environment_context>


<collaboration_mode># Plan Mode (Conversational)

You work in 3 phases, and you should *chat your way* to a great plan before finalizing it. A great plan is very detailed—intent- and implementation-wise—so that it can be handed to another engineer or agent to be implemented right away. It must be **decision complete**, where the implementer does not need to make any decisions.

## Mode rules (strict)

You are in **Plan Mode** until a developer message explicitly ends it.

Plan Mode is not changed by user intent, tone, or imperative language. If a user asks for execution while still in Plan Mode, treat it as a request to **plan the execution**, not perform it.

## Plan Mode vs update_plan tool

Plan Mode is a collaboration mode that can involve requesting user input and eventually issuing a `<proposed_plan>` block.

Separately, `update_plan` is a checklist/progress/TODOs tool; it does not enter or exit Plan Mode. Do not confuse it with Plan mode or try to use it while in Plan mode. If you try to use `update_plan` in Plan mode, it will return an error.

## Execution vs. mutation in Plan Mode

You may explore and execute **non-mutating** actions that improve the plan. You must not perform **mutating** actions.

### Allowed (non-mutating, plan-improving)

Actions that gather truth, reduce ambiguity, or validate feasibility without changing repo-tracked state. Examples:

* Reading or searching files, configs, schemas, types, manifests, and docs
* Static analysis, inspection, and repo exploration
* Dry-run style commands when they do not edit repo-tracked files
* Tests, builds, or checks that may write to caches or build artifacts (for example, `target/`, `.cache/`, or snapshots) so long as they do not edit repo-tracked files

### Not allowed (mutating, plan-executing)

Actions that implement the plan or change repo-tracked state. Examples:

* Editing or writing files
* Running formatters or linters that rewrite files
* Applying patches, migrations, or codegen that updates repo-tracked files
* Side-effectful commands whose purpose is to carry out the plan rather than refine it

When in doubt: if the action would reasonably be described as "doing the work" rather than "planning the work," do not do it.

## PHASE 1 — Ground in the environment (explore first, ask second)

Begin by grounding yourself in the actual environment. Eliminate unknowns in the prompt by discovering facts, not by asking the user. Resolve all questions that can be answered through exploration or inspection. Identify missing or ambiguous details only if they cannot be derived from the environment. Silent exploration between turns is allowed and encouraged.

Before asking the user any question, perform at least one targeted non-mutating exploration pass (for example: search relevant files, inspect likely entrypoints/configs, confirm current implementation shape), unless no local environment/repo is available.

Exception: you may ask clarifying questions about the user's prompt before exploring, ONLY if there are obvious ambiguities or contradictions in the prompt itself. However, if ambiguity might be resolved by exploring, always prefer exploring first.

Do not ask questions that can be answered from the repo or system (for example, "where is this struct?" or "which UI component should we use?" when exploration can make it clear). Only ask once you have exhausted reasonable non-mutating exploration.

## PHASE 2 — Intent chat (what they actually want)

* Keep asking until you can clearly state: goal + success criteria, audience, in/out of scope, constraints, current state, and the key preferences/tradeoffs.
* Bias toward questions over guessing: if any high-impact ambiguity remains, do NOT plan yet—ask.

## PHASE 3 — Implementation chat (what/how we’ll build)

* Once intent is stable, keep asking until the spec is decision complete: approach, interfaces (APIs/schemas/I/O), data flow, edge cases/failure modes, testing + acceptance criteria, rollout/monitoring, and any migrations/compat constraints.

## Asking questions

Critical rules:

* Strongly prefer using the `request_user_input` tool to ask any questions.
* Offer only meaningful multiple‑choice options; don’t include filler choices that are obviously wrong or irrelevant.
* In rare cases where an unavoidable, important question can’t be expressed with reasonable multiple‑choice options (due to extreme ambiguity), you may ask it directly without the tool.

You SHOULD ask many questions, but each question must:

* materially change the spec/plan, OR
* confirm/lock an assumption, OR
* choose between meaningful tradeoffs.
* not be answerable by non-mutating commands.

Use the `request_user_input` tool only for decisions that materially change the plan, for confirming important assumptions, or for information that cannot be discovered via non-mutating exploration.

## Two kinds of unknowns (treat differently)

1. **Discoverable facts** (repo/system truth): explore first.

   * Before asking, run targeted searches and check likely sources of truth (configs/manifests/entrypoints/schemas/types/constants).
   * Ask only if: multiple plausible candidates; nothing found but you need a missing identifier/context; or ambiguity is actually product intent.
   * If asking, present concrete candidates (paths/service names) + recommend one.
   * Never ask questions you can answer from your environment (e.g., “where is this struct”).

2. **Preferences/tradeoffs** (not discoverable): ask early.

   * These are intent or implementation preferences that cannot be derived from exploration.
   * Provide 2–4 mutually exclusive options + a recommended default.
   * If unanswered, proceed with the recommended option and record it as an assumption in the final plan.

## Finalization rule

Only output the final plan when it is decision complete and leaves no decisions to the implementer.

When you present the official plan, wrap it in a `<proposed_plan>` block so the client can render it specially:

1) The opening tag must be on its own line.
2) Start the plan content on the next line (no text on the same line as the tag).
3) The closing tag must be on its own line.
4) Use Markdown inside the block.
5) Keep the tags exactly as `<proposed_plan>` and `</proposed_plan>` (do not translate or rename them), even if the plan content is in another language.

Example:

<proposed_plan>
plan content
</proposed_plan>

plan content should be human and agent digestible. The final plan must be plan-only and include:

* A clear title
* A brief summary section
* Important changes or additions to public APIs/interfaces/types
* Test cases and scenarios
* Explicit assumptions and defaults chosen where needed

Do not ask "should I proceed?" in the final output. The user can easily switch out of Plan mode and request implementation if you have included a `<proposed_plan>` block in your response. Alternatively, they can decide to stay in Plan mode and continue refining the plan.

Only produce at most one `<proposed_plan>` block per turn, and only when you are presenting a complete spec.

If the user stays in Plan mode and asks for revisions after a prior `<proposed_plan>`, any new `<proposed_plan>` must be a complete replacement.
</collaboration_mode>


Both checkpoints are downloaded. Now let me provide the comprehensive architectural analysis you need.

---

# X-Codec 2.0 Complete Architecture Analysis

## High-Level Data Flow

The model is an **Encoder-VQ-Decoder** neural audio codec operating at **16kHz, mono** with a **hop length of 320** samples (20ms per frame, 50 tokens/second). The key innovation is fusing **acoustic** and **semantic** representations before quantization.

```
                                    TRAINING FLOW
┌─────────────┐
│ Audio 16kHz  │──┬──► CodecEncoder (Conv) ──────────────► acoustic_emb [B, 1024, T']
│  [B, 1, T]  │  │                                              │
└─────────────┘  │                                              │
                 │                                              ▼
                 └──► Wav2Vec2-BERT (frozen) ──► SemanticEncoder ──► semantic_emb [B, 1024, T']
                      (layer 16 hidden)                         │
                                                                ▼
                                                    cat ──► [B, 2048, T']
                                                                │
                                                    fc_prior (Linear 2048→2048)
                                                                │
                                                    ┌───────────▼────────────┐
                                                    │  ResidualFSQ           │
                                                    │  levels=[4]*8 = 65536  │
                                                    │  1 quantizer, dim=2048 │
                                                    └───────────┬────────────┘
                                                                │
                                                    vq_post_emb [B, 2048, T']
                                                       │                │
                                            fc_post_a  │                │  fc_post_s
                                         (2048→1024)   │                │  (2048→1024)
                                                       ▼                ▼
                                                 VocosBackbone    SemanticDecoder
                                                 (12-layer TF)    (3-layer Conv)
                                                       │                │
                                                  ISTFTHead        semantic_recon
                                                 (n_fft=1280)     (L2 loss vs target)
                                                       │
                                                  Recon Audio
                                                  [B, 1, T]
```

Where `T' = T / 320` (the temporal compression ratio).

---

## 1. CODEC ENCODER (`CodecEncoder`)

**File**: `vq/codec_encoder.py` lines 29-99
**Purpose**: Compress raw waveform into a dense acoustic embedding
**Architecture origin**: BigCodec encoder with Snake activations + alias-free processing

### Input/Output
- **Input**: `[B, 1, T]` — raw mono audio at 16kHz
- **Output**: `[B, T/320, 1024]` — acoustic embedding (permuted to `[B, T', H]`)

### Layer-by-layer Breakdown

```
Layer 0: WNConv1d(1 → 48, kernel=7, pad=3)         # Weight-normed, initial projection
         ↓ [B, 48, T]

Layer 1: EncoderBlock(48→96, stride=2)              # Downsample 2x
         ↓ [B, 96, T/2]

Layer 2: EncoderBlock(96→192, stride=2)             # Downsample 2x
         ↓ [B, 192, T/4]

Layer 3: EncoderBlock(192→384, stride=4)            # Downsample 4x
         ↓ [B, 384, T/16]

Layer 4: EncoderBlock(384→768, stride=4)            # Downsample 4x
         ↓ [B, 768, T/64]

Layer 5: EncoderBlock(768→1536, stride=5)           # Downsample 5x
         ↓ [B, 1536, T/320]

Final:   SnakeBeta(1536) → WNConv1d(1536→1024, k=3, p=1)
         ↓ [B, 1024, T/320]

Permute: .permute(0, 2, 1) → [B, T/320, 1024]
```

**Total downsampling**: `2 × 2 × 4 × 4 × 5 = 320` (matches STFT hop_length)

### EncoderBlock Internal Structure

Each `EncoderBlock(dim_in→dim_out, stride)` contains:

```
3x ResidualUnit(dim_in/2, dilations=[1, 3, 9])
    Each ResidualUnit:
        SnakeBeta(dim) → WNConv1d(dim, dim, k=7, dilation=d) → SnakeBeta(dim) → WNConv1d(dim, dim, k=1)
        + residual connection

SnakeBeta(dim_in/2)
WNConv1d(dim_in/2 → dim_out, kernel=2*stride, stride=stride)     # Strided conv for downsampling
```

The dilations `[1, 3, 9]` give effective receptive fields of `[7, 19, 55]` per residual unit, capturing multi-scale temporal patterns.

### SnakeBeta Activation

```
SnakeBeta(x) = x + (1/β) * sin²(x * α)
```
- `α` (frequency) and `β` (magnitude) are **learnable per-channel** parameters
- Initialized in log-space (`alpha_logscale=True`) so `α = exp(param)`, `β = exp(param)`
- Adds periodic inductive bias suited for audio signals

### Alias-Free Activation (`Activation1d`)

Every SnakeBeta in the encoder is wrapped in `Activation1d`:

```
x → UpSample1d(2x, Kaiser-sinc filter) → SnakeBeta(x) → DownSample1d(2x, Kaiser-sinc LPF)
```

This prevents aliasing artifacts when applying nonlinear activations to bandlimited signals. The Kaiser-sinc filters are designed with cutoff=0.25 (relative to Nyquist) and half-width=0.3.

---

## 2. SEMANTIC BRANCH

### 2a. Wav2Vec2-BERT (`facebook/w2v-bert-2.0`)

**Frozen** pretrained model. 580M parameters, trained on 4.5M hours of 143+ languages.

- **Input**: Feature-extracted audio via `AutoFeatureExtractor` (pads 160 samples on each side)
- **Output used**: `hidden_states[16]` — the 16th transformer layer's hidden state
- Shape: `[B, T', 1024]` where `T'` matches the codec frame rate

### 2b. SemanticEncoder

**File**: `vq/module.py` lines 308-374
**Purpose**: Adapt Wav2Vec2-BERT features into the codec's representation space

```
Conv1d(1024→1024, k=3, p=1, no bias)      # Initial projection
    ↓
Residual Block:
    ReLU → Conv1d(1024→1024, k=3, p=1) → ReLU → Conv1d(1024→1024, k=3, p=1)
    + skip connection
    ↓
Conv1d(1024→1024, k=3, p=1, no bias)      # Final projection
```

- Input: `[B, 1024, T']` (Wav2Vec2 layer-16 features, transposed)
- Output: `[B, 1024, T']` (adapted semantic embedding)

### 2c. SemanticDecoder

**File**: `vq/module.py` lines 376-420
**Mirror architecture** of SemanticEncoder, reconstructs semantic features from VQ output for the semantic reconstruction loss.

---

## 3. FUSION & QUANTIZATION

### 3a. Feature Concatenation & Projection

```python
vq_emb = torch.cat([semantic_target_processed, vq_emb], dim=1)  # [B, 2048, T']
vq_emb = fc_prior(vq_emb.transpose(1, 2)).transpose(1, 2)      # Linear(2048→2048)
```

The `fc_prior` is a single `nn.Linear(2048, 2048)` that mixes the concatenated acoustic + semantic embeddings.

### 3b. Finite Scalar Quantization (ResidualFSQ)

**File**: `vq/codec_decoder_vocos.py` lines 389-393
**From**: `vector_quantize_pytorch.ResidualFSQ`

```python
ResidualFSQ(
    dim=2048,
    levels=[4, 4, 4, 4, 4, 4, 4, 4],  # 8 scalars, each with 4 levels
    num_quantizers=1                     # Single quantizer (no residual stages)
)
```

**How FSQ works**:
- Projects the 2048-dim vector into 8 scalar dimensions
- Each scalar is quantized to one of 4 levels (via rounding)
- Total codebook size: `4^8 = 65,536` entries
- **No codebook collapse** — achieves ~99% codebook utilization
- Uses straight-through gradient estimator for backprop
- **No commitment loss** needed (FSQ returns `None` for vq_loss)

**Input**: `[B, T', 2048]` (permuted before passing)
**Output**: `vq_post_emb [B, 2048, T']`, `vq_code [B, T', 1]` (single integer code per frame)

### 3c. Post-VQ Projections

```python
fc_post_a = nn.Linear(2048, 1024)   # Project VQ output for acoustic decoder
fc_post_s = nn.Linear(2048, 1024)   # Project VQ output for semantic decoder
```

---

## 4. VOCOS DECODER

### 4a. VocosBackbone

**File**: `vq/codec_decoder_vocos.py` lines 294-358
**Purpose**: Transform quantized embeddings into STFT-domain features

```
Input: [B, T', 1024] (from fc_post_a output)

Layer 1: Transpose → Conv1d(1024→1024, k=7, p=3) → embed
         ↓ [B, 1024, T']

Layer 2: prior_net — 2× ResnetBlock(1024→1024, dropout=0.1)
         ↓ [B, 1024, T']

Layer 3: Transpose → 12× TransformerBlock(dim=1024, heads=16, RoPE dim=64)
         ↓ [B, T', 1024]

Layer 4: Transpose → post_net — 2× ResnetBlock(1024→1024, dropout=0.1)
         ↓ [B, 1024, T']

Layer 5: Transpose → LayerNorm(1024)
         ↓ [B, T', 1024]
```

### ResnetBlock (within VocosBackbone)

```
x → GroupNorm(32 groups) → Swish → Conv1d(k=3) → GroupNorm(32 groups) → Swish → Dropout(0.1) → Conv1d(k=3)
  + residual shortcut
```

### TransformerBlock

**File**: `vq/bs_roformer5.py` lines 91-110
**Architecture**: Pre-norm Transformer with RoPE + Flash Attention + SiLU MLP

```
x → RMSNorm(1024) → Attention → + residual
  → RMSNorm(1024) → MLP       → + residual
```

**Attention** (lines 43-88):
```
Input: [B, T', 1024]

c_attn: Linear(1024 → 3072, no bias)    # Joint Q/K/V projection
    → rearrange to q,k,v: [B, 16 heads, T', 64]

RoPE: RotaryPositionalEmbeddings(dim=64) applied to Q and K
    (from torchtune — standard RoPE with complex rotation)

Flash Attention: scaled_dot_product_attention(q, k, v, causal=False)
    → [B, 16, T', 64]

rearrange → [B, T', 1024]
c_proj: Linear(1024 → 1024, no bias)    # Output projection
```

**MLP** (lines 28-40):
```
fc1: Linear(1024 → 4096, no bias)       # 4× expansion
SiLU activation                          # x * sigmoid(x)
fc2: Linear(4096 → 1024, no bias)       # Project back
```

**RMSNorm** (lines 14-24):
```
output = x * rsqrt(mean(x²) + eps) * weight     # Simpler than LayerNorm, no mean subtraction
```

### 4b. ISTFTHead

**File**: `vq/codec_decoder_vocos.py` lines 104-149
**Purpose**: Convert transformer output to time-domain audio via inverse STFT

```
Input: [B, T', 1024]

Linear(1024 → 1282)              # n_fft + 2 = 1280 + 2
    ↓ [B, T', 1282]

Transpose → [B, 1282, T']

chunk(2, dim=1):
    mag: [B, 641, T']            # Magnitude (n_fft/2 + 1 bins)
    p:   [B, 641, T']            # Phase

mag = exp(mag)                   # Log-magnitude → linear magnitude
mag = clip(mag, max=1e2)         # Safety clamp

Complex spectrum: S = mag * (cos(p) + j*sin(p))    # Polar → rectangular

ISTFT:
    n_fft=1280, hop_length=320, win_length=1280
    "same" padding (not center)
    Hann window
    Overlap-add → [B, T]
```

The ISTFT uses a custom implementation (lines 17-85) that supports "same" padding for neural vocoding, unlike PyTorch's native `torch.istft` which only supports "center" padding.

**Output**: `[B, 1, T]` — reconstructed waveform

---

## 5. DISCRIMINATORS (Training Only)

### 5a. Multi-Period Discriminator (MPD)

**File**: `module/mpd.py`
5 parallel discriminators with periods `[2, 3, 5, 7, 11]`

Each `HiFiGANPeriodDiscriminator(period=p)`:
```
Input: [B, 1, T]

Reshape: [B, 1, T/p, p]         # Fold waveform by period

5× Conv2d layers (downsampling along time, keeping period dim):
    Conv2d(in→out, kernel=(5,1), stride=(3,1)) → LeakyReLU(0.1)
    channels: 16 → 64 → 256 → 512 → 512 → 512

Output Conv2d(512→1, kernel=(2,1))
Flatten → scalar prediction per frame
```

Returns intermediate feature maps for feature matching loss.

### 5b. Multi-STFT Spectral Discriminator

**File**: `module/mstft.py`
8 parallel discriminators with FFT sizes: `[78, 126, 206, 334, 542, 876, 1418, 2296]`

These are **deliberately non-standard FFT sizes** tailored for the transformer decoder's characteristics.

Each `NLayerSpecDiscriminator`:
```
Input: STFT magnitude [B, 1, F, T']    (computed externally per FFT size)

Conv2d(1→32, k=5, stride=2) → LeakyReLU(0.2)

3× downsampling:
    Conv2d(in→out, k=2*scale+1, stride=scale) → LeakyReLU(0.2)
    scales: [2, 2, 2]
    channels: 32 → 64 → 128 → 256

Conv2d(256→512, k=3) → LeakyReLU(0.2)
Conv2d(512→1, k=3)   # Final prediction
```

---

## 6. LOSS FUNCTIONS

### Generator Total Loss:
```
L_gen = 15 * L_mel + 1 * L_adv + 1 * L_fm + 5 * L_semantic + L_vq
```

| Loss | Weight | Description |
|------|--------|-------------|
| `L_mel` | 15.0 | Multi-resolution mel spectrogram loss (L1 on mel spectrograms at multiple scales) |
| `L_adv` | 1.0 | LS-GAN adversarial loss from both MPD + Spec discriminators |
| `L_fm` | 1.0 | Feature matching loss (L1 on discriminator intermediate features) |
| `L_semantic` | 5.0 | L2 between reconstructed semantic features and original Wav2Vec2 features |
| `L_vq` | — | FSQ quantization loss (None for FSQ, handled internally) |

### Discriminator Loss:
```
L_disc = 1.0 * (L_real + L_fake)
```
Standard LS-GAN: `L_real = mean((D(y) - 1)²)`, `L_fake = mean(D(ŷ)²)`

---

## 7. KEY DIMENSIONS SUMMARY

| Component | Input Shape | Output Shape | Parameters |
|-----------|------------|--------------|------------|
| **CodecEncoder** | `[B, 1, T]` | `[B, T/320, 1024]` | ~8M |
| **Wav2Vec2-BERT** (frozen) | `[B, feat, T_feat]` | `[B, T', 1024]` | ~580M (frozen) |
| **SemanticEncoder** | `[B, 1024, T']` | `[B, 1024, T']` | ~6M |
| **fc_prior** | `[B, T', 2048]` | `[B, T', 2048]` | ~4.2M |
| **ResidualFSQ** | `[B, T', 2048]` | `[B, T', 2048]` + codes | ~small |
| **fc_post_a** | `[B, T', 2048]` | `[B, T', 1024]` | ~2.1M |
| **VocosBackbone** | `[B, T', 1024]` | `[B, T', 1024]` | ~100M |
| **ISTFTHead** | `[B, T', 1024]` | `[B, 1, T]` | ~1.3M |
| **SemanticDecoder** | `[B, 1024, T']` | `[B, 1024, T']` | ~6M |
| **fc_post_s** | `[B, T', 2048]` | `[B, T', 1024]` | ~2.1M |
| **MPD** (5 period discs) | `[B, 1, T]` | per-disc preds | ~7M |
| **SpecDisc** (8 STFT discs) | `[B, 1, T]` | per-disc preds | ~15M |

---

## 8. CRITICAL RESOLUTION-RELEVANT DETAILS

These are the elements that directly constrain or determine the output resolution:

1. **Temporal compression = 320x**: Set by `up_ratios=[2,2,4,4,5]` in encoder. At 16kHz, this yields 50 frames/sec.

2. **ISTFT reconstruction**: `n_fft=1280`, `hop_length=320`, `win_length=1280`. The ISTFTHead outputs `n_fft + 2 = 1282` values per frame, split into 641 magnitude + 641 phase bins. This means the maximum representable frequency is `16000/2 = 8kHz` (Nyquist at 16kHz SR).

3. **The bottleneck**: The VQ layer operates at `[B, 2048, T/320]`. Every 320 input samples are compressed to a single 2048-dim vector, then quantized to one of 65,536 codes.

4. **Transformer receptive field**: 12 layers of self-attention with RoPE can attend to the entire sequence, giving global context at the compressed frame rate.

5. **No upsampling convolutions in decoder**: Unlike traditional vocoders (HiFi-GAN), the decoder uses ISTFT for waveform synthesis, so the temporal upsampling is implicit in the ISTFT operation. The transformer operates entirely at the compressed rate `T/320`.

6. **Frequency resolution**: With `n_fft=1280` and `sr=16000`, frequency bin resolution = `16000/1280 = 12.5 Hz`. The 641 bins span `[0, 8000] Hz`.

---

## 9. INFERENCE PIPELINE (for context)

From `inference.py`, the exact inference steps are:

```python
# 1. Pad audio to multiple of 320
wav = F.pad(wav, (0, 320 - wav.shape[1] % 320))

# 2. Extract features for Wav2Vec2
feat = feature_extractor(F.pad(wav, (160, 160)), sr=16000)

# 3. Encode acoustic
vq_emb = encoder(wav.unsqueeze(1))                    # [B, T/320, 1024]
vq_emb = vq_emb.transpose(1, 2)                       # [B, 1024, T/320]

# 4. Extract semantic
semantic = semantic_model(feat).hidden_states[16]      # [B, T', 1024]
semantic = SemanticEncoder(semantic.transpose(1, 2))   # [B, 1024, T']

# 5. Fuse & quantize
combined = cat([semantic, vq_emb], dim=1)              # [B, 2048, T']
combined = fc_prior(combined.T(1,2)).T(1,2)            # [B, 2048, T']
_, vq_code, _ = decoder(combined, vq=True)             # Quantize → codes

# 6. Decode from codes
vq_post_emb = decoder.quantizer.get_output_from_indices(vq_code.T(1,2))
vq_post_emb = fc_post_a(vq_post_emb.T(1,2)).T(1,2)   # [B, 1024, T']
recon = decoder(vq_post_emb.T(1,2), vq=False)         # VocosBackbone → ISTFT
```

---

This gives you the complete context of every layer, shape, activation, and design choice in X-Codec 2.0. The critical bottlenecks for resolution scaling are: (a) the 320x compression ratio in the encoder, (b) the ISTFT head's `n_fft=1280` tied to 16kHz, and (c) the VQ operating at a fixed 2048-dim per frame at 50 fps. When discussing super-resolution with another model, these three constraints are the primary targets to modify or bypass.

Now that you have all the context on entire architecture of the model and inference as well. Now the outputs of this model is in 16khz. which sounds a little muffled or tunnely, cause ofcourse it's 16khz and not super quality. i ofcourse dont wanna add any bottleneck to overall decoding inference as i am going to use this in voice agents and a TTS platform, so would want any diffusion style decoding bottlenecks and need smooth and effective methods and worked and what's novel and where does research leads us and what new methods can i try and what worked and did anyone try stuff like this that isnt diffusion ? or if diffusion is the solution, are there any methods i can use to not add any latency or overhead or not-so much extra compute requirements than the orignal decoder requirements i mean it comes at a price, but not drastically overheads. and also, if it requires retraining the entire decoder module or parts or it, i am ready to do it. have the hifi-tts2 data encoded and ready since we wont be touching teh encoder layers, i can reuse the pre-encoded data and can run experimetns. please direct me towards effective reserach methods i can implement and take over the upsampling problem with high fidelity and superior quality. and if controllability with gating for conditional 16/24/32/48 is possible, that's still an option preferrably willing to explore.

above is the prompt for different agents that i provided. Now, instructions for you.

You are my Architecture Integrator + Principal Engineer.

Goal:
I will provide 3–4 architecture plans from different agents. Your job is to produce ONE upgraded ensemble architecture that accounts for EVERY suggestion from every plan, with explicit traceability and robust evaluation. No suggestion may be ignored.

Operating rules:
1) You must create a “Suggestion Inventory” that captures every distinct suggestion/upgrade/idea/concern from every plan. Assign each a unique ID (e.g., A1, A2… for Plan A; B1… for Plan B; etc.).
2) You must produce a “Coverage Matrix” table where each suggestion ID is marked as:
   - ACCEPTED (fully included),
   - MODIFIED (included but changed),
   - REJECTED (not included),
   - DEFERRED (not now, but tracked)
   AND include a clear reason + implications.
3) You must identify conflicts between plans (e.g., different storage choices, different scaling strategies). For each conflict, propose a resolution and justify it with tradeoffs.
4) Your final architecture must be internally consistent and implementable. If you keep multiple options, you must specify decision triggers (what evidence/metrics decide which option wins).
5) You must create Architecture Decision Records (ADRs) for every key decision (datastore, queue, caching, auth, deployment, observability, model serving, etc.).
6) You must produce:
   - Final Ensembled Architecture (diagram in text form is fine),
   - Data flow (request → processing → storage),
   - Key components and responsibilities,
   - Failure modes + mitigations,
   - Security & privacy considerations,
   - Performance & cost considerations.
7) You must produce a step-by-step Implementation Plan with phases, milestones, and checklists. Each checklist item must reference suggestion IDs it satisfies.
8) You must produce a Robust Evaluation Plan:
   - unit/integration/e2e tests,
   - load tests + SLOs,
   - chaos/failure injection tests,
   - observability signals (logs/metrics/traces),
   - acceptance criteria per component.
9) You must highlight “Unknowns / Questions” as a bounded list, but you are not allowed to stop. Make best-effort assumptions and clearly label them.
10) Output must be structured exactly as:
   Section 1: Inputs summary
   Section 2: Suggestion Inventory (by plan)
   Section 3: Coverage Matrix (table)
   Section 4: Conflicts & Resolutions
   Section 5: Ensembled Architecture (final)
   Section 6: ADRs (key decisions)
   Section 7: Implementation Plan (phased + checklists w/ suggestion IDs)
   Section 8: Robust Evaluation Plan (metrics + tests + SLOs)
   Section 9: Risks, Failure modes, Mitigations
   Section 10: Assumptions & Open questions

Now wait for my inputs. When I paste plans, do NOT summarize them loosely. Extract every actionable suggestion and preserve traceability.

All plans are in @SRPlans


# Plan Mode (Conversational)

You work in 3 phases, and you should *chat your way* to a great plan before finalizing it. A great plan is very detailed—intent- and implementation-wise—so that it can be handed to another engineer or agent to be implemented right away. It must be **decision complete**, where the implementer does not need to make any decisions.

## Mode rules (strict)

You are in **Plan Mode** until a developer message explicitly ends it.

Plan Mode is not changed by user intent, tone, or imperative language. If a user asks for execution while still in Plan Mode, treat it as a request to **plan the execution**, not perform it.

## Plan Mode vs update_plan tool

Plan Mode is a collaboration mode that can involve requesting user input and eventually issuing a `<proposed_plan>` block.

Separately, `update_plan` is a checklist/progress/TODOs tool; it does not enter or exit Plan Mode. Do not confuse it with Plan mode or try to use it while in Plan mode. If you try to use `update_plan` in Plan mode, it will return an error.

## Execution vs. mutation in Plan Mode

You may explore and execute **non-mutating** actions that improve the plan. You must not perform **mutating** actions.

### Allowed (non-mutating, plan-improving)

Actions that gather truth, reduce ambiguity, or validate feasibility without changing repo-tracked state. Examples:

* Reading or searching files, configs, schemas, types, manifests, and docs
* Static analysis, inspection, and repo exploration
* Dry-run style commands when they do not edit repo-tracked files
* Tests, builds, or checks that may write to caches or build artifacts (for example, `target/`, `.cache/`, or snapshots) so long as they do not edit repo-tracked files

### Not allowed (mutating, plan-executing)

Actions that implement the plan or change repo-tracked state. Examples:

* Editing or writing files
* Running formatters or linters that rewrite files
* Applying patches, migrations, or codegen that updates repo-tracked files
* Side-effectful commands whose purpose is to carry out the plan rather than refine it

When in doubt: if the action would reasonably be described as "doing the work" rather than "planning the work," do not do it.

## PHASE 1 — Ground in the environment (explore first, ask second)

Begin by grounding yourself in the actual environment. Eliminate unknowns in the prompt by discovering facts, not by asking the user. Resolve all questions that can be answered through exploration or inspection. Identify missing or ambiguous details only if they cannot be derived from the environment. Silent exploration between turns is allowed and encouraged.

Before asking the user any question, perform at least one targeted non-mutating exploration pass (for example: search relevant files, inspect likely entrypoints/configs, confirm current implementation shape), unless no local environment/repo is available.

Exception: you may ask clarifying questions about the user's prompt before exploring, ONLY if there are obvious ambiguities or contradictions in the prompt itself. However, if ambiguity might be resolved by exploring, always prefer exploring first.

Do not ask questions that can be answered from the repo or system (for example, "where is this struct?" or "which UI component should we use?" when exploration can make it clear). Only ask once you have exhausted reasonable non-mutating exploration.

## PHASE 2 — Intent chat (what they actually want)

* Keep asking until you can clearly state: goal + success criteria, audience, in/out of scope, constraints, current state, and the key preferences/tradeoffs.
* Bias toward questions over guessing: if any high-impact ambiguity remains, do NOT plan yet—ask.

## PHASE 3 — Implementation chat (what/how we’ll build)

* Once intent is stable, keep asking until the spec is decision complete: approach, interfaces (APIs/schemas/I/O), data flow, edge cases/failure modes, testing + acceptance criteria, rollout/monitoring, and any migrations/compat constraints.

## Asking questions

Critical rules:

* Strongly prefer using the `request_user_input` tool to ask any questions.
* Offer only meaningful multiple‑choice options; don’t include filler choices that are obviously wrong or irrelevant.
* In rare cases where an unavoidable, important question can’t be expressed with reasonable multiple‑choice options (due to extreme ambiguity), you may ask it directly without the tool.

You SHOULD ask many questions, but each question must:

* materially change the spec/plan, OR
* confirm/lock an assumption, OR
* choose between meaningful tradeoffs.
* not be answerable by non-mutating commands.

Use the `request_user_input` tool only for decisions that materially change the plan, for confirming important assumptions, or for information that cannot be discovered via non-mutating exploration.

## Two kinds of unknowns (treat differently)

1. **Discoverable facts** (repo/system truth): explore first.

   * Before asking, run targeted searches and check likely sources of truth (configs/manifests/entrypoints/schemas/types/constants).
   * Ask only if: multiple plausible candidates; nothing found but you need a missing identifier/context; or ambiguity is actually product intent.
   * If asking, present concrete candidates (paths/service names) + recommend one.
   * Never ask questions you can answer from your environment (e.g., “where is this struct”).

2. **Preferences/tradeoffs** (not discoverable): ask early.

   * These are intent or implementation preferences that cannot be derived from exploration.
   * Provide 2–4 mutually exclusive options + a recommended default.
   * If unanswered, proceed with the recommended option and record it as an assumption in the final plan.

## Finalization rule

Only output the final plan when it is decision complete and leaves no decisions to the implementer.

When you present the official plan, wrap it in a `<proposed_plan>` block so the client can render it specially:

1) The opening tag must be on its own line.
2) Start the plan content on the next line (no text on the same line as the tag).
3) The closing tag must be on its own line.
4) Use Markdown inside the block.
5) Keep the tags exactly as `<proposed_plan>` and `</proposed_plan>` (do not translate or rename them), even if the plan content is in another language.

Example:

<proposed_plan>
plan content
</proposed_plan>

plan content should be human and agent digestible. The final plan must be plan-only and include:

* A clear title
* A brief summary section
* Important changes or additions to public APIs/interfaces/types
* Test cases and scenarios
* Explicit assumptions and defaults chosen where needed

Do not ask "should I proceed?" in the final output. The user can easily switch out of Plan mode and request implementation if you have included a `<proposed_plan>` block in your response. Alternatively, they can decide to stay in Plan mode and continue refining the plan.

Only produce at most one `<proposed_plan>` block per turn, and only when you are presenting a complete spec.

If the user stays in Plan mode and asks for revisions after a prior `<proposed_plan>`, any new `<proposed_plan>` must be a complete replacement.

Summary: auto

## Skills
A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.
### Available skills
- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)
- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)
### How to use skills
- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.
- How to use a skill (progressive disclosure):
  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
  2) When `SKILL.md` references relative paths (e.g., `scripts/foo.py`), resolve them relative to the skill directory listed above first, and only consider other paths if needed.
  3) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  4) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  5) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Coordination and sequencing:
  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.
  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.


**Acknowledging readiness and inspecting plans folder**


I’ll wait for your plan inputs and enforce full traceability exactly in your 10-section format. I’m doing a quick non-mutating check to see whether `@SRPlans` already exists in the workspace so I can ingest it immediately when you’re ready.


pwd && ls -la && find . -maxdepth 4 -iname '*srplans*' -o -iname '@SRPlans'


Output:
Chunk ID: 4928c8
Wall time: 0.0511 seconds
Process exited with code 0
Original token count: 397
Output:
/home/ubuntu/xcodec-sr
total 140
drwxrwxr-x 13 ubuntu ubuntu  4096 Mar  5 07:41 .
drwxr-x--- 60 ubuntu ubuntu  4096 Mar  5 09:29 ..
drwxrwxr-x  3 ubuntu ubuntu  4096 Mar  5 07:19 .cache
-rw-rw-r--  1 ubuntu ubuntu   394 Mar  5 07:12 .env
drwxrwxr-x  8 ubuntu ubuntu  4096 Mar  5 07:10 .git
-rw-rw-r--  1 ubuntu ubuntu  1064 Mar  5 07:10 LICENSE
-rw-rw-r--  1 ubuntu ubuntu  3099 Mar  5 07:10 README.md
drwxrwxr-x  2 ubuntu ubuntu  4096 Mar  5 09:19 SRPlans
drwxrwxr-x  2 ubuntu ubuntu  4096 Mar  5 07:19 ckpt
drwxrwxr-x  3 ubuntu ubuntu  4096 Mar  5 07:10 common
drwxrwxr-x  6 ubuntu ubuntu  4096 Mar  5 07:10 config
drwxrwxr-x  2 ubuntu ubuntu  4096 Mar  5 07:10 criterions
-rw-rw-r--  1 ubuntu ubuntu  4267 Mar  5 07:10 data_module.py
-rw-rw-r--  1 ubuntu ubuntu  2315 Mar  5 07:10 get_tsv.py
-rw-rw-r--  1 ubuntu ubuntu  5884 Mar  5 07:10 inference.py
-rw-rw-r--  1 ubuntu ubuntu  8256 Mar  5 07:10 inference_save_code.py
-rw-rw-r--  1 ubuntu ubuntu  1130 Mar  5 07:10 large_scale_save_code.sh
-rw-rw-r--  1 ubuntu ubuntu 15869 Mar  5 07:10 lightning_module.py
drwxrwxr-x  2 ubuntu ubuntu  4096 Mar  5 07:10 module
-rw-rw-r--  1 ubuntu ubuntu  1038 Mar  5 07:10 preprocess.py
-rw-rw-r--  1 ubuntu ubuntu  2430 Mar  5 07:10 requirements.txt
drwxrwxr-x  4 ubuntu ubuntu  4096 Mar  5 07:10 test_audio
-rw-rw-r--  1 ubuntu ubuntu  2421 Mar  5 07:10 train.py
-rw-rw-r--  1 ubuntu ubuntu   337 Mar  5 07:10 train_slurm.sh
-rw-rw-r--  1 ubuntu ubuntu  4265 Mar  5 07:10 utils.py
drwxrwxr-x  5 ubuntu ubuntu  4096 Mar  5 07:18 venv
drwxrwxr-x  3 ubuntu ubuntu  4096 Mar  5 07:10 vq
./SRPlans


Summary: auto


**Preparing to prompt plan source**


I found `SRPlans` in the repo; next I’ll inspect its file list so we can lock the exact source of truth before I build the inventory and coverage matrix.


ls -la /home/ubuntu/xcodec-sr/SRPlans && find /home/ubuntu/xcodec-sr/SRPlans -maxdepth 2 -type f | sort


Output:
Chunk ID: 904456
Wall time: 0.0504 seconds
Process exited with code 0
Original token count: 151
Output:
total 80
drwxrwxr-x  2 ubuntu ubuntu  4096 Mar  5 09:19 .
drwxrwxr-x 13 ubuntu ubuntu  4096 Mar  5 07:41 ..
-rw-rw-r--  1 ubuntu ubuntu 19163 Mar  5 07:55 Claude.md
-rw-rw-r--  1 ubuntu ubuntu  7987 Mar  5 07:48 GPT.md
-rw-rw-r--  1 ubuntu ubuntu 15319 Mar  5 09:19 GPT_PRO.md
-rw-rw-r--  1 ubuntu ubuntu  5793 Mar  5 07:41 Gemini.md
-rw-rw-r--  1 ubuntu ubuntu 16673 Mar  5 07:51 perplexity.md
/home/ubuntu/xcodec-sr/SRPlans/Claude.md
/home/ubuntu/xcodec-sr/SRPlans/GPT.md
/home/ubuntu/xcodec-sr/SRPlans/GPT_PRO.md
/home/ubuntu/xcodec-sr/SRPlans/Gemini.md
/home/ubuntu/xcodec-sr/SRPlans/perplexity.md


**Planning user input confirmation**


I found five plan files in `SRPlans` (`Claude.md`, `GPT.md`, `GPT_PRO.md`, `Gemini.md`, `perplexity.md`). I need one decision to avoid mismatched traceability before I extract suggestion IDs.


Question: Which source should be authoritative for this integration pass?

Choices:
I’ll treat all five files in /home/ubuntu/xcodec-sr/SRPlans as canonical inputs now.
I’ll ignore local files and only process plans you paste in chat.
I’ll use local files now and later append/merge any pasted updates with new IDs.


Output: {"answers":{"input_source":{"answers":["Use SRPlans files (Recommended)"]}}}


**Planning comprehensive file extraction**


I’ll ingest all five `SRPlans` files as canonical and build a full suggestion ledger before drafting the ensemble. I’m starting with a structured extraction pass (headings, numbered items, and action verbs) to ensure no suggestion is dropped.


Output:
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Output:
  149 /home/ubuntu/xcodec-sr/SRPlans/Claude.md
  148 /home/ubuntu/xcodec-sr/SRPlans/GPT.md
  188 /home/ubuntu/xcodec-sr/SRPlans/GPT_PRO.md
   82 /home/ubuntu/xcodec-sr/SRPlans/Gemini.md
  238 /home/ubuntu/xcodec-sr/SRPlans/perplexity.md
  805 total


Output:
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Output:
===== Claude.md =====
1:# Upsampling neural codec output from 16kHz to 48kHz: a complete technical guide
7:## The information-theoretic argument for why this works
17:## Strategy 1: Direct ISTFTHead rescaling — the simplest path to 24kHz
38:## Strategy 2: Multi-band ISTFT with PQMF synthesis for 48kHz
52:## Strategy 3: Modular post-hoc bandwidth extension
66:## Strategy 4: Single-step generation models for maximum quality
78:## How production systems actually handle this problem
90:## Training recipe for the frozen-encoder scenario
98:- Multi-resolution mel-spectrogram L1 loss across 5–7 mel-bin counts (32, 64, 128, 256, 512) and multiple STFT configurations — this is the primary reconstruction signal
99:- Multi-resolution STFT loss (spectral convergence + log magnitude) at configurations like (2048,512), (1024,256), (512,128), (256,64)
100:- GAN adversarial loss (least-squares formulation) from all discriminators
101:- Feature matching loss (L1 on intermediate discriminator features)
102:- If operating in spectral domain: amplitude spectrum MSE + anti-wrapping phase losses (instantaneous phase error, group delay continuity, instantaneous frequency continuity)
108:1. **Warmup (0–20k steps)**: Reconstruction losses only (no GAN). Learning rate 2×10⁻⁴ for decoder with AdamW. This stabilizes the modified output dimensions before adversarial training.
109:2. **GAN activation (20k–100k steps)**: Gradually introduce discriminator losses. Use separate Adam optimizer for discriminators (lr=2×10⁻⁴). EnCodec's gradient balancer mechanism (where each loss weight defines its fraction of the overall gradient) helps with loss balancing.
110:3. **Full training (100k–400k steps)**: All losses active. Exponential LR decay (~0.999996 per step). SNAC demonstrates stable training without gradient clipping when using depthwise convolutions.
116:## Prioritized implementation roadmap
122:1. **Direct ISTFTHead rescaling to 24kHz** (n_fft=2048, hop=480). Directly validated by X-Codec 2.0 modification paper. Initialize via weight interpolation. Fine-tune 100–200k steps. Expected result: +0.29 MOS or better over 16kHz baseline. RTF ~0.001 on GPU.
124:2. **AP-BWE as post-hoc module** for 16kHz→48kHz. Pre-existing open-source code and methodology. Train on codec output → 48kHz target pairs. RTF ~0.003 on GPU, ~0.055 on CPU. Completely modular — no changes to existing codec.
128:3. **Multi-band ISTFTHead (4 bands) for 48kHz**. Each sub-band at 12kHz with PQMF synthesis. Reuse existing 16kHz head architecture where possible. Enables sub-band-specific discriminators. More architectural work but better frequency decomposition for 48kHz.
130:4. **MusicHiFi-BWE-style separate upsampler**. HiFi-GAN generator with 3× upsampling + skip connection from input. ~1786× real-time. Simple to implement and proven for generative model post-processing.
134:5. **FLowHigh single-step flow matching** for mel-spectrogram super-resolution + Vocos/BigVGAN vocoder. SOTA metrics on benchmarks. RTF ~0.07 — tight but within budget.
136:6. **Consistency model distillation** of a diffusion-based audio SR model. No dedicated implementation exists yet, but CoMoSpeech demonstrates the approach achieves RTF ~0.007 for TTS — the same distillation framework applies to super-resolution.
140:7. **Controllable multi-resolution decoder** with sample-rate conditioning embedding (extending Vocos's existing bandwidth_id mechanism). Train a single decoder that outputs 16/24/32/48kHz based on a conditioning signal. Requires more training data diversity but offers maximum deployment flexibility.
142:8. **VQ-based upsampling** where a small transformer predicts 48kHz codec tokens from 16kHz codec tokens. Interesting but autoregressive prediction creates a latency bottleneck unless non-autoregressive methods (masked prediction) are used.
146:## Conclusion

===== GPT.md =====
7:## 1) The highest-ROI move: a **post BWE module** after your 16 kHz decoder
17:### Proven blueprints (GAN, fast, non-diffusion)
28:### How to train this without wasting your life
34:1. For each HiFi-TTS2 clip, you already have **codes**.
35:2. Decode them with your current decoder → **ŷ16k_codec**.
36:3. Train BWE model: **ŷ16k_codec → y48k_gt** (or y32k_gt).
51:## 2) Even better: **high-band residual head** (cheap, controllable, codec-aware)
71:### AP-BWE: amplitude+phase parallel prediction (fast)
79:## 3) The “cleanest” but most invasive: retrain the decoder to output 24/32/48 kHz directly
109:## 4) If diffusion is “the solution” but you refuse latency: use **distilled / one-step diffusion**
125:## 5) Controllability: one model for 16/24/32/48
131:1. **Target-SR embedding** (FiLM-style or additive conditioning): give the SR module a learned embedding for `{16,24,32,48}` so it learns “how much band to invent.” NU-Wave 2 does bandwidth conditioning (different mechanism, same idea).
132:2. **Predict bands progressively**: always output base 16k, then optionally add a learned 8–16k band (for 32k), then optionally add 16–24k (for 48k). This maps nicely to a subband/PQMF design.
136:## What I would do in your shoes (minimal wasted time)
138:1. **Target 32 kHz first**, not 48. It’s where speech “opens up” the most per FLOP.
139:2. Train a **post-BWE** model on **codec-decoded 16k** → 32k/48k ground truth, using the HiFi-GAN+ style losses/discriminators (upperband-weighted).
140:3. If you want extra polish: switch to an **amplitude+phase** high-band predictor like AP-BWE style. ([arXiv][1])
141:4. Only if you still hate it: retrain the ISTFTHead geometry and make a native 32/48k decoder.

===== GPT_PRO.md =====
7:1. **24 kHz decoder-only retrain first**
8:2. **Residual high-band restorer conditioned on codec latents**
9:3. **A stronger non-diffusion decoder replacement with harmonic/phase priors**
10:4. **Single-step latent flow or distilled diffusion only as an optional HQ tier**
12:### What has actually worked in the literature
22:### So what should *you* build?
24:#### 1) Start with a **24 kHz decoder-only retarget**, not 48 kHz
44:#### 2) Your easiest production-friendly win is a **residual high-band decoder branch**
50:1. Keep your current 16 kHz decoder exactly as the **base low-band path**.
51:2. Deterministically upsample it to 24/32/48 kHz with a good sinc/polyphase resampler.
52:3. Add a tiny **high-band residual model** that predicts only the missing band in STFT space.
53:4. Condition that branch on:
68:#### 3) Add **harmonic / voicing structure** explicitly
80:### If you want a stronger decoder replacement, these are the best bets
95:### Conditional 16 / 24 / 32 / 48 is absolutely possible
112:### Losses and training tricks that matter here
129:### How I would structure the experiments
153:### Where diffusion fits, if you really want it
165:### My final recommendation
169:1. **Train a 24 kHz decoder-only variant first** using your cached codes and original high-rate waveforms.
170:2. **Replace the current head with a dual amplitude/phase or harmonic-prior decoder**, not just a naïve bigger ISTFT head.
171:3. **Add a residual high-band branch with low-band bypass**, conditioned on `vq_post_emb` and F0/UV.
172:4. **Only then** build a cascaded 24 -> 48 stage if 24 kHz still isn’t enough.
173:5. Keep **single-step latent flow/diffusion** as an optional HQ path, not the default.

===== Gemini.md =====
9:## 1. Multi-Band Vocos Reconstruction (The "Sub-band" Approach)
22:## 2. Periodic Signal Modeling with SnakeBeta (BigVGAN-style Decoder)
30:## 3. GAN-based Bandwidth Extension (BWE) with Neural Source-Filter
41:## 4. Gated Multi-Resolution Output (Controllability)
55:## Comparison of Methods for Voice Agents
66:## Strategic Recommendation for MayaResearch
70:1. **Keep the Encoder/VQ**: Do not touch them. Your 50Hz frame rate is perfect.
71:2. **Upgrade the Decoder**:
79:### Why "Inworld-style" might have failed you:

===== perplexity.md =====
1:# Upsampling Strategies for X-Codec 2.0: From 16kHz to High-Fidelity Audio
3:## Executive Summary
7:## Strategy 1: Decoder-Only Retraining with Modified ISTFT Parameters
9:### The Proven Baseline: X-Codec 2.0 → 24kHz
13:- **Freeze all encoder components** (CodecEncoder, Wav2Vec2-BERT, SemanticEncoder) and the quantizer
14:- **Increase the decoder hop size** from 320 to 960 samples
15:- **Add an AvgPool1d(kernel=2, stride=2)** before vector quantization to reduce the latent rate from 50Hz to 25Hz
16:- **Retrain only the decoder** (VocosBackbone + ISTFTHead) to output 24kHz audio
17:- **Interpolate decoder weights** from the pretrained checkpoint using 1D linear interpolation on the output projection
21:### Extending to 48kHz: Direct ISTFT Head Modification
42:## Strategy 2: GAN-Based Bandwidth Extension Post-Filter
44:### AP-BWE: The Speed Champion
48:- **Architecture:** Dual-stream ConvNeXt backbone with parallel amplitude and phase prediction streams plus inter-stream connections
49:- **Speed:** 292.3× faster than real-time on a single RTX 4090 GPU, and 18.1× faster on a single CPU core for 16kHz→48kHz[5]
50:- **Quality:** State-of-the-art on both 16kHz and 48kHz target rates
51:- **Waveform synthesis:** Direct iSTFT from predicted amplitude + phase (no external vocoder needed)
52:- **Parameter count:** Lightweight all-CNN architecture with no attention layers
58:### MS-BWE: Multi-Stage Cascading
62:- Each stage extends one frequency band (e.g., 16kHz→24kHz→48kHz)
63:- One-stage GPU speed: ~1271× real-time (4× faster than full AP-BWE per stage)
64:- Enables **controllable output rate**: stop at any intermediate stage for 24kHz, or run all stages for 48kHz
68:### HiFi-GAN+ (Bandwidth Extension Is All You Need)
72:### UBGAN: Ultra-Lightweight
76:- **Causal** with 20ms frame size and 5ms look-ahead
77:- Significantly fewer parameters and lower complexity than AP-BWE
78:- Both blind (no side information) and guided (with small side channel) variants
79:- Designed to generalize across multiple codec types and bitrates
83:## Strategy 3: Flow Matching Post-Filter (Low-Overhead Generative)
85:### FLowHigh: Single-Step Flow Matching
89:- **Architecture:** 2-layer transformer (16 heads, 1024 dim, 4096 FFN) = 35.4M parameters
90:- **RTF:** 0.1769 (single step)—13.6–24.4× faster than diffusion models requiring 50–100 steps[14]
91:- **Quality:** Outperforms NVSR, AudioSR, Nu-wave2, UDM+, mdctGAN, and Fre-painter across all input rates on the VCTK benchmark
92:- **External vocoder:** Uses BigVGAN (pretrained at 48kHz, 256 mel bins) for final waveform synthesis
104:### FlowDec: Integrated Codec Post-Filter
108:- Reduces required DNN evaluations from 60 (ScoreDec) to **6** without distillation
109:- Achieves FAD scores better than DAC at 48kHz
110:- Operates at 4–7.5 kbps
111:- Designed as an integral part of the codec, not a separate module
113:### CodecFlow: Latent-Space BWE
117:- Exploits the observation that LR and HR speech remain closely aligned in continuous codec latent space
118:- Uses a structure-constrained RVQ for improved latent alignment
119:- End-to-end fine-tuned with the codec
120:- Achieves strong spectral fidelity on both 8→16kHz and 8→44.1kHz tasks
124:## Strategy 4: Modified Decoder Architecture
126:### Multi-Band iSTFT Synthesis
130:- Operates in a few-frequency space, then upsamples in frequency dimension
131:- Faster and more lightweight than standard iSTFTNet
132:- Avoids the quality degradation seen when replacing too many upsampling layers with iSTFT
138:### WaveNeXt: Replace iSTFT with Learned Linear
142:### ComplexDec: Complex Spectral Domain
146:- Uses complex STFT input/output (320 hop, 510 n_fft, Hann window)
147:- 150 Hz frame rate with 256 embedding dimension
148:- 16 codebooks at 24 kbps
149:- Avoids information loss from waveform-domain compression
150:- Demonstrates strong out-of-domain robustness
154:## Strategy 5: Controllable Multi-Rate Output
156:### Gated Resolution with Conditional ISTFT Head
160:1. **Shared VocosBackbone** processes the VQ output at frame rate
161:2. **Multiple ISTFT heads** with different `n_fft`/`hop_length` configurations
162:3. **Gated selection** at inference time based on desired output resolution
166:### MS-BWE Cascading for Progressive Enhancement
176:### FlexiCodec-Style Dynamic Rate
180:## Recommended Implementation Roadmap
182:### Phase 1: Quick Win (1–2 weeks)
185:- Freeze encoders + quantizer
186:- Add AvgPool1d(k=2, stride=2) before quantization
187:- Increase hop to 960, update ISTFT params for 24kHz
188:- Interpolate decoder weights from pretrained checkpoint
189:- Fine-tune decoder on your HiFi-TTS2 pre-encoded data
190:- Expected: +0.29 MOS, 12kHz Nyquist, eliminates most "muffled" quality
192:### Phase 2: Full-Band Post-Filter (2–4 weeks)
205:### Phase 3: Integrated Decoder Redesign (4–8 weeks)
208:- Widen VocosBackbone to 1536 or 2048 hidden dim
209:- Increase transformer layers from 12 to 16
210:- Set ISTFT head to n_fft=3840, hop=960, producing 1921 freq bins
211:- Add multi-scale sub-band CQT discriminator (from BigVGAN-v2)
212:- Train with multi-resolution mel loss at 48kHz
214:### Phase 4: Multi-Rate Gating (Optional, +2 weeks on Phase 3)
216:- Add parallel ISTFT heads for 16k/24k/48k
217:- Condition on a resolution embedding fed into the VocosBackbone
218:- Train jointly with random resolution sampling per batch
220:## Methods Comparison
233:## Key Takeaways


**Planning exhaustive suggestion extraction**


I’ve mapped the major headings; now I’m extracting every actionable item line-by-line from each plan so the inventory is complete and auditable. I’ll process files in chunks and then reconcile duplicates vs true conflicts.


Output:
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Output:
     1	You’re not “doing it wrong” as much as you’re running into physics + incentives: your **16 kHz codec literally cannot carry >8 kHz**, so any “48 kHz” output is **bandwidth extension** (educated hallucination). The win condition is: *generate high-band that sounds natural, doesn’t wreck timbre, and doesn’t add latency you’ll hate later.*
     2
     3	Here are the **methods that actually work in the real world** (non-diffusion first), and how to bolt them onto *your* X-Codec2 setup.
     4
     5	---
     6
     7	## 1) The highest-ROI move: a **post BWE module** after your 16 kHz decoder
     8
     9	Keep X-Codec2 exactly as-is. Decode to 16 kHz (your current Vocos+ISTFT), then run a **tiny “16→32/48 kHz” bandwidth extension net**.
    10
    11	Why this is the sweet spot:
    12
    13	* You don’t touch the codec/quantizer.
    14	* You can make it **streaming + causal**, with **single-digit ms algorithmic delay**.
    15	* You can ship **16 kHz fast path** and **32/48 kHz HQ path** as a toggle.
    16
    17	### Proven blueprints (GAN, fast, non-diffusion)
    18
    19	**HiFi-GAN+ for BWE (a.k.a. “Bandwidth Extension is All You Need”)**
    20	They treat BWE like speech enhancement and use a **feed-forward WaveNet-ish conv stack** + **GAN**, and explicitly call out why naive L1/L2 kills high frequency (it averages unpredictable HF noise to zero). They use **multi-FFT log-spectrogram losses** and an **upperband-focused mel loss**, plus **spectral + waveform discriminators**.
    21
    22	**BBWEXNet (real-time CPU, causal, 16 ms delay)**
    23	A **causal U-Net** style model designed for **16 kHz → 48 kHz** streaming, with optimizations like **sample-shuffle decoders** and VQ-ish bottlenecks for efficiency. They explicitly measure real-time feasibility and report **max ~16 ms algorithmic delay**.
    24
    25	**Key practical note:** for speech, most of the perceived “not muffled anymore” gain is **reconstructing 8–16 kHz**, not obsessing about >16 kHz. BBWEXNet says that directly.
    26	So if you want the best quality/compute trade: **target 32 kHz first**, then optionally add a 32→48 refinement later.
    27
    28	### How to train this without wasting your life
    29
    30	**Don’t train BWE on clean “downsampled” audio and then feed it codec audio at inference.** That domain gap is how you get crunchy or pointless results.
    31
    32	Do this instead:
    33
    34	1. For each HiFi-TTS2 clip, you already have **codes**.
    35	2. Decode them with your current decoder → **ŷ16k_codec**.
    36	3. Train BWE model: **ŷ16k_codec → y48k_gt** (or y32k_gt).
    37
    38	   * Input is upsampled to target rate with causal sinc (or polyphase). BBWEXNet does causal sinc upsampling as a preprocessing step.
    39
    40	Loss recipe that consistently stops “still muffled”:
    41
    42	* **Multi-resolution log-STFT losses** (several FFT sizes)
    43	* **Upperband-weighted mel loss** (only care about missing band strongly)
    44	* **Spectral discriminator** (on mel) + **multi-rate waveform discriminators**
    45	  This combo exists because pure regression losses will happily erase the exact high-frequency texture you’re trying to add.
    46
    47	If you only implement one thing from this whole message: **upperband-weighted loss + adversarial**. That’s the difference between “meh upsample” and “wait this sounds HD”.
    48
    49	---
    50
    51	## 2) Even better: **high-band residual head** (cheap, controllable, codec-aware)
    52
    53	Instead of generating full 48 kHz waveform from scratch, do:
    54
    55	* Keep your decoder output as “low band” anchor.
    56	* Predict **only the missing band** as a residual:
    57
    58	  * 16k → 48k: missing is ~8–24 kHz
    59	  * 16k → 32k: missing is ~8–16 kHz (often the sweet spot)
    60
    61	Implementation patterns that work:
    62
    63	* **PQMF / subband split**: generate only the top subbands, then recombine.
    64	* Or **STFT-domain high-band prediction**: predict high-frequency magnitude + phase residual.
    65
    66	Why this fits your architecture weirdly well:
    67
    68	* Your decoder already works in an STFT-ish space (it predicts mag+phase then ISTFT).
    69	* There’s newer BWE work that explicitly models **amplitude + phase** in parallel for quality *and* speed.
    70
    71	### AP-BWE: amplitude+phase parallel prediction (fast)
    72
    73	AP-BWE predicts high-frequency amplitude residuals **and** phase (not just magnitude), uses GAN discriminators in waveform and spectral domains, and reports extremely fast generation (including CPU real-time multipliers). ([arXiv][1])
    74
    75	This is basically “do what your ISTFTHead wants, but only for the missing band”.
    76
    77	---
    78
    79	## 3) The “cleanest” but most invasive: retrain the decoder to output 24/32/48 kHz directly
    80
    81	This can work surprisingly well if you accept that HF is generated, not recovered.
    82
    83	The trick is: keep your **token rate = 50 Hz**, but change ISTFT geometry to match the target sample rate while keeping 20 ms hop.
    84
    85	Use:
    86
    87	* `hop_length = target_sr / 50`
    88
    89	  * 24k → 480
    90	  * 32k → 640
    91	  * 48k → 960
    92	* Keep your window length in *time* consistent (you used 80 ms at 16k: 1280 samples). So:
    93
    94	  * 24k: `n_fft ≈ 1920`
    95	  * 32k: `n_fft ≈ 2560`
    96	  * 48k: `n_fft ≈ 3840`
    97
    98	That keeps frequency resolution ~12.5 Hz like you currently have, just extended upward.
    99
   100	Cost impact is not catastrophic:
   101
   102	* Your ISTFT head linear goes from `1024→1282` to `1024→3842` at 48k. That’s ~3× params in that layer, not 10×.
   103	* Backbone compute stays at 50 Hz.
   104
   105	Training must change though: you *must* add **high-band-focused losses/discriminators**, or it’ll learn to be “safe” and stay dull.
   106
   107	---
   108
   109	## 4) If diffusion is “the solution” but you refuse latency: use **distilled / one-step diffusion**
   110
   111	You can get diffusion-like quality without 50+ steps, but it’s still extra compute.
   112
   113	Two relevant directions:
   114
   115	* **NU-Wave 2**: diffusion SR that’s explicitly built to handle **multiple input sample rates with conditioning** (bandwidth conditioning via BSFT). Great for your “16/24/32/48 controllability” idea, but diffusion baseline is slower.
   116	* **FlashSR**: **one-step** diffusion via distillation for 48 kHz SR. This is exactly “diffusion, but not slow,” at least compared to classic samplers. ([arXiv][2])
   117
   118	A very practical hybrid that teams actually ship:
   119
   120	* Train a **heavy teacher** (diffusion SR) offline.
   121	* Distill into a **GAN / conv student** that runs real-time.
   122
   123	---
   124
   125	## 5) Controllability: one model for 16/24/32/48
   126
   127	This is doable, and there’s precedent.
   128
   129	Two ways:
   130
   131	1. **Target-SR embedding** (FiLM-style or additive conditioning): give the SR module a learned embedding for `{16,24,32,48}` so it learns “how much band to invent.” NU-Wave 2 does bandwidth conditioning (different mechanism, same idea).
   132	2. **Predict bands progressively**: always output base 16k, then optionally add a learned 8–16k band (for 32k), then optionally add 16–24k (for 48k). This maps nicely to a subband/PQMF design.
   133
   134	---
   135
   136	## What I would do in your shoes (minimal wasted time)
   137
   138	1. **Target 32 kHz first**, not 48. It’s where speech “opens up” the most per FLOP.
   139	2. Train a **post-BWE** model on **codec-decoded 16k** → 32k/48k ground truth, using the HiFi-GAN+ style losses/discriminators (upperband-weighted).
   140	3. If you want extra polish: switch to an **amplitude+phase** high-band predictor like AP-BWE style. ([arXiv][1])
   141	4. Only if you still hate it: retrain the ISTFTHead geometry and make a native 32/48k decoder.
   142
   143	That path is boring, reproducible, and doesn’t require you to gamble your real-time product on diffusion sampling steps.
   144
   145	If you implement the post-BWE with **codec-audio-as-input** and **upperband-weighted adversarial training**, you’re going to stop feeling like you’re “wasting time” and start feeling like you’re shipping. Which is unfortunately the whole point.
   146
   147	[1]: https://arxiv.org/html/2401.06387v1 "Towards High-Quality and Efficient Speech Bandwidth Extension with Parallel Amplitude and Phase Prediction"
   148	[2]: https://arxiv.org/html/2501.10807v1 "FlashSR: One-step Versatile Audio Super-resolution via Diffusion Distillation This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00222383)."


Output:
Chunk ID: 107052
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Output:
     1	The muffled quality you are experiencing is the "Nyquist ceiling." At 16kHz, your audio is capped at 8kHz of effective bandwidth, which cuts off the high-frequency fricatives and "air" that characterize high-fidelity speech.
     2
     3	Since you are building for voice agents and a TTS platform, **latency is your primary constraint**. Diffusion, even with consistency models or distillation (1-2 steps), often introduces overhead in the iterative sampling loop that can push you past the real-time threshold for interactive agents.
     4
     5	Here are the most effective research-backed methods to achieve high-fidelity upsampling (24kHz to 48kHz) while maintaining a low-latency, non-diffusion decoder.
     6
     7	---
     8
     9	## 1. Multi-Band Vocos Reconstruction (The "Sub-band" Approach)
    10
    11	Instead of forcing a single ISTFT head to reconstruct the entire 0-24kHz spectrum, you can utilize **Multi-Band Synthesis**. This technique is used in models like MB-MelGAN and is highly effective for bandwidth extension.
    12
    13	* **The Concept**: Keep your current 16kHz tokens as the "base." Train a new decoder that predicts multiple sub-bands (e.g., 0-8kHz, 8-16kHz, 16-24kHz).
    14	* **Why it works**: High frequencies are often more "noise-like" or periodic in specific ways. By isolating the high-frequency reconstruction, the model doesn't "smear" the base band with high-frequency artifacts.
    15	* **Implementation**:
    16	* Modify the `ISTFTHead` to output multiple sets of magnitude/phase pairs (one for each band).
    17	* Use a **Pseudo-Quadrature Mirror Filter (PQMF)** bank to synthesize the final 48kHz waveform from these bands.
    18	* **Latency**: Minimal. PQMF synthesis is a series of simple convolutions.
    19
    20
    21
    22	## 2. Periodic Signal Modeling with SnakeBeta (BigVGAN-style Decoder)
    23
    24	Since you already have `SnakeBeta` in your encoder, you should leverage its periodic inductive bias for the upsampling task. Standard convolutions struggle to generate high-frequency harmonics that don't exist in the bottleneck.
    25
    26	* **The Method**: Replace the Vocos Transformer's output projection with a series of **Upsampling Blocks** using **SnakeBeta** activations and **Alias-free convolutions**.
    27	* **Novelty**: Most upsamplers fail because they alias high frequencies back into the audible range. By using the alias-free processing (Kaiser-sinc filters) you already have in the `Activation1d` module, you can push the resolution to 44.1kHz or 48kHz without "metallic" artifacts.
    28	* **Retraining**: You can freeze the `CodecEncoder` and `Wav2Vec2-BERT` and only train the `VocosBackbone` and the new upsampling head using a Multi-Resolution Mel Loss + Multi-Period Discriminator (MPD) operating at the higher sample rate.
    29
    30	## 3. GAN-based Bandwidth Extension (BWE) with Neural Source-Filter
    31
    32	Research from models like **APNet** or **StreamSpeech** suggests that explicit source-filter decomposition helps in high-resolution synthesis.
    33
    34	* **Technique**:
    35	* The decoder predicts a **Source** (excitation) and a **Filter** (envelope) at 48kHz.
    36	* The source can be generated using a periodic generator (for voiced parts) and noise (for unvoiced parts).
    37	* This is much more "stable" than trying to predict raw ISTFT bins for 48kHz, which requires a very large `n_fft` (e.g., 2048 or 4096) and increases the hop-length/latency.
    38
    39
    40
    41	## 4. Gated Multi-Resolution Output (Controllability)
    42
    43	To achieve your 16/24/32/48kHz flexibility without multiple models, you can implement **Resolution Gating**.
    44
    45	* **The Architecture**:
    46	* Use a shared backbone (your current Transformer).
    47	* Implement **Multiple Output Heads**: Head A (16kHz), Head B (24kHz), etc.
    48	* **Gating**: Use a conditional embedding (a scalar or one-hot vector) passed into the Transformer and the heads.
    49	* **Training**: Train with "Resolution Dropout"—randomly downsample your 48kHz ground truth to 16kHz or 24kHz during training, and force the model to reconstruct the original 48kHz. This teaches the model to "hallucinate" high frequencies when the input is low-res, but remain faithful when the input is high-res.
    50
    51
    52
    53	---
    54
    55	## Comparison of Methods for Voice Agents
    56
    57	| Method | Complexity | Latency | Quality Gain | Retraining Effort |
    58	| --- | --- | --- | --- | --- |
    59	| **Multi-Band ISTFT** | Medium | Very Low | High | High (Decoder only) |
    60	| **SnakeBeta Upsampling** | Low | Low | Medium-High | Medium (Head only) |
    61	| **Source-Filter GAN** | High | Low | Very High | High (Full Decoder) |
    62	| **Consistency Models** | High | Medium | Extreme | Very High |
    63
    64	---
    65
    66	## Strategic Recommendation for MayaResearch
    67
    68	Since you already have the `hifi-tts2` data encoded, I suggest the **Multi-Band ISTFT** route combined with **SnakeBeta**.
    69
    70	1. **Keep the Encoder/VQ**: Do not touch them. Your 50Hz frame rate is perfect.
    71	2. **Upgrade the Decoder**:
    72	* Increase the `VocosBackbone` width slightly if needed.
    73	* Change the `ISTFTHead` to target 24kHz or 48kHz.
    74	* **Crucial**: If you keep the hop length at 320 for 16kHz tokens, but want 48kHz output, your effective hop length in the output is $320 \times 3 = 960$.
    75	* Use **Sub-band Discriminators**: Train with discriminators that specifically look at the 8-16kHz and 16-24kHz ranges (high-pass filtered) to force the model to generate energy in those bands.
    76
    77
    78
    79	### Why "Inworld-style" might have failed you:
    80
    81	Most "plug-and-play" upsamplers are generic. Because X-Codec 2.0 uses **FSQ (Finite Scalar Quantization)**, the bottleneck is extremely tight. A generic upsampler expects a "dirty" 16kHz signal with some high-frequency noise it can amplify. FSQ is too "clean"—it zero-outs everything above the Nyquist. You need a decoder that **generates** new content based on the semantic/acoustic tokens, not just an upsampler that filters the output.
    82
    83	Would you like me to draft a PyTorch implementation for a Multi-Band ISTFT head that fits your current `VocosBackbone`?


I’ve extracted GPT/Gemini suggestions and concerns. Next I’m doing full-pass extraction on `Claude.md`, `GPT_PRO.md`, and `perplexity.md`, then I’ll build the traceability matrix with accept/modify/reject/defer and explicit rationale.