Output:
Exit code: 0
Wall time: 0 seconds
Output:
transcribe/eval_indicwhisper.py:135:                indicwhisper_wer = compute_wer(ref_norm, indicwhisper_norm)["wer"]
transcribe/eval_indicwhisper.py:136:                gemini_wer = compute_wer(ref_norm, gemini_norm)["wer"]
transcribe/eval_indicwhisper.py:137:                consensus_wer = compute_wer(ref_norm, consensus_norm)["wer"]
transcribe/quick_eval.py:177:        "wer": compute_wer(reference, hypothesis),
transcribe/quick_eval.py:251:                wer_result = compute_wer(reference, hypothesis)
transcribe/quick_eval.py:333:                w_wer = compute_wer(r.reference, r.model_outputs["whisper"])["wer"]
transcribe/quick_eval.py:338:                g_wer = compute_wer(r.reference, r.model_outputs["gemini"])["wer"]
transcribe/utils/text_processing.py:136:def compute_wer(reference: str, hypothesis: str) -> dict:
transcribe/test_basic.py:34:    wer_result = compute_wer(ref, hyp)
transcribe/full_pipeline_eval.py:264:        w_wer = compute_wer(ref_norm, normalize_for_wer(whisper_text, language))["wer"]
transcribe/full_pipeline_eval.py:265:        g_wer = compute_wer(ref_norm, normalize_for_wer(gemini_text, language))["wer"]
transcribe/full_pipeline_eval.py:266:        c_wer = compute_wer(ref_norm, normalize_for_wer(consensus_text, language))["wer"]
transcribe/tests/test_utils.py:43:        result = compute_wer("hello world", "hello world")
transcribe/tests/test_utils.py:47:        result = compute_wer("hello", "goodbye")
transcribe/tests/test_utils.py:51:        result = compute_wer("hello", "hello world")
transcribe/tests/test_utils.py:55:        result = compute_wer("hello world", "hello")
transcribe/tests/test_utils.py:59:        result = compute_wer("", "hello")
transcribe/tests/test_utils.py:63:        result = compute_wer("hello world", "")
transcribe/tests/test_utils.py:69:        result = compute_wer(ref, hyp)
transcribe/evaluation/metrics.py:57:    wer_result = compute_wer(reference, hypothesis)
transcribe/quick_eval_gemini.py:175:                wer_result = compute_wer(ref_norm, hyp_norm)


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) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  4) 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.


**Considering normalization update impact**

Updating tokenize_for_wer to use normalize_for_wer will change WER normalization globally but likely stay compatible with existing tests, as basic cases like "Hello" vs. "hello" should still pass; next I'll check test_utils to confirm no breaking test effects.


sed -n '1,120p' transcribe/tests/test_utils.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
"""Tests for utility functions."""

import pytest
from ..utils.text_processing import (
    normalize_text,
    compute_wer,
    compute_cer,
    levenshtein_distance,
    tokenize_for_wer,
    detect_script,
)

class TestNormalization:
    """Test text normalization."""

    def test_basic_normalization(self):
        text = "  Hello   World  "
        result = normalize_text(text)
        assert result == "Hello World"

    def test_unicode_normalization(self):
        # Devanagari text
        text = "नमस्ते"
        result = normalize_text(text)
        assert result == "नमस्ते"

    def test_telugu_text(self):
        text = "నమస్కారం"
        result = normalize_text(text)
        assert result == "నమస్కారం"

    def test_lowercase(self):
        text = "Hello World"
        result = normalize_text(text, lowercase=True)
        assert result == "hello world"

class TestWER:
    """Test Word Error Rate computation."""

    def test_identical(self):
        result = compute_wer("hello world", "hello world")
        assert result["wer"] == 0.0

    def test_complete_mismatch(self):
        result = compute_wer("hello", "goodbye")
        assert result["wer"] == 1.0  # 1 substitution / 1 word

    def test_insertion(self):
        result = compute_wer("hello", "hello world")
        assert result["insertions"] == 1

    def test_deletion(self):
        result = compute_wer("hello world", "hello")
        assert result["deletions"] == 1

    def test_empty_reference(self):
        result = compute_wer("", "hello")
        assert result["wer"] == 1.0

    def test_empty_hypothesis(self):
        result = compute_wer("hello world", "")
        assert result["deletions"] == 2

    def test_hindi_wer(self):
        ref = "मैं घर जा रहा हूं"
        hyp = "मैं घर जा रहा हूँ"
        result = compute_wer(ref, hyp)
        # One word difference (हूं vs हूँ)
        assert result["substitutions"] == 1

class TestCER:
    """Test Character Error Rate computation."""

    def test_identical(self):
        result = compute_cer("hello", "hello")
        assert result["cer"] == 0.0

    def test_one_char_diff(self):
        result = compute_cer("hello", "hallo")
        assert result["substitutions"] == 1

    def test_telugu_cer(self):
        ref = "నమస్కారం"
        hyp = "నమస్కారము"
        result = compute_cer(ref, hyp)
        assert result["cer"] > 0  # Some difference

class TestScriptDetection:
    """Test script detection."""

    def test_devanagari(self):
        text = "नमस्ते मेरा नाम है"
        assert detect_script(text) == "devanagari"

    def test_telugu(self):
        text = "నమస్కారం నా పేరు"
        assert detect_script(text) == "telugu"

    def test_tamil(self):
        text = "வணக்கம் என் பெயர்"
        assert detect_script(text) == "tamil"

    def test_latin(self):
        text = "Hello my name is"
        assert detect_script(text) == "latin"

    def test_mixed_prefers_dominant(self):
        text = "Hello नमस्ते world"
        # Should detect based on character count
        script = detect_script(text)
        assert script in ["latin", "devanagari"]

class TestTokenization:
    """Test tokenization for WER."""


Summary: auto


sed -n '120,240p' transcribe/tests/test_utils.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:

    def test_simple_tokenization(self):
        tokens = tokenize_for_wer("hello world")
        assert tokens == ["hello", "world"]

    def test_indic_tokenization(self):
        tokens = tokenize_for_wer("नमस्ते दुनिया")
        assert len(tokens) == 2

    def test_code_mixed(self):
        tokens = tokenize_for_wer("मैं office जाता हूं")
        assert len(tokens) == 4
        assert "office" in tokens

if __name__ == "__main__":
    pytest.main([__file__, "-v"])


**Planning normalization update in text processing**

Tests show lowering and punctuation removal in normalization won't break WER tests, so the plan is to enhance normalization by adding punctuation stripping, digit unification, and whitespace cleanup in text_processing, then update tokenize_for_wer to use this improved normalization before applying changes to utils/text_processing.py.


**Drafting patch for enhanced normalization**

Planning to add digit normalization converting all digits to ASCII, remove all punctuation (including underscores) by Unicode category, strip zero-width characters, and collapse whitespace in normalize_for_wer, then update tokenize_for_wer to use this enhanced normalization without changing its signature.


Input:
*** Begin Patch
*** Update File: transcribe/utils/text_processing.py
@@
 def normalize_for_wer(text: str, language: str = "unknown") -> str:
     """
     Normalize text specifically for WER computation.

     Applies aggressive normalization to ensure fair comparison:
     - Lowercase
     - Remove punctuation
-    - Normalize numbers
+    - Normalize numbers (map all digit scripts to ASCII)
     - Collapse whitespace
     """
     if not text:
         return ""

     # Unicode normalization
     text = unicodedata.normalize("NFC", text)

     # Lowercase
     text = text.lower()

-    # Remove common punctuation but keep Indic punctuation
-    text = re.sub(r'[.,!?;:\'"()\[\]{}<>«»""''—–-]', ' ', text)
+    # Normalize digits across scripts (e.g., Devanagari -> ASCII)
+    normalized_digits = []
+    for ch in text:
+        if ch.isdigit():
+            try:
+                normalized_digits.append(str(unicodedata.digit(ch)))
+            except (TypeError, ValueError):
+                normalized_digits.append(ch)
+        else:
+            normalized_digits.append(ch)
+    text = "".join(normalized_digits)
+
+    # Remove punctuation (unicode category "P", including Indic danda)
+    text = "".join(
+        " " if unicodedata.category(ch).startswith("P") else ch
+        for ch in text
+    )
+
+    # Remove zero-width characters
+    text = re.sub(r"[\u200b-\u200d\uFEFF]", "", text)

     # Normalize whitespace
     text = re.sub(r'\s+', ' ', text).strip()

     return text
@@
 def tokenize_for_wer(text: str) -> List[str]:
     """
     Tokenize text for WER calculation.
     Handles Indic scripts properly by not splitting on script boundaries.
     """
     # Normalize first
-    text = normalize_text(text)
+    text = normalize_for_wer(text)
*** End Patch


Output: {"output":"Success. Updated the following files:\nM transcribe/utils/text_processing.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


<turn_aborted>
  <turn_id>3</turn_id>
  <reason>interrupted</reason>
  <guidance>The user interrupted the previous turn. Do not continue or repeat work from that turn unless the user explicitly asks. If any tools/commands were aborted, they may have partially executed; verify current state before retrying.</guidance>
</turn_aborted>


# Context from my IDE setup:

## Open tabs:
- .env: .env
- PROMPT.md: PROMPT.md
- claude.md: docs/transcribe/claude.md
- kimi.md: docs/kimi.md
- gpt.md: docs/transcribe/gpt.md

## My request for Codex:
wait i actually dont dislike the outputs from gemini tbh. why are we considering it to be false ? why can't we accept gemini answers ? i just noticed they're all not wrong. just needed a better prompting. but infact the current one is also indeed not bad.


<turn_aborted>
  <turn_id>6</turn_id>
  <reason>interrupted</reason>
  <guidance>The user interrupted the previous turn. Do not continue or repeat work from that turn unless the user explicitly asks. If any tools/commands were aborted, they may have partially executed; verify current state before retrying.</guidance>
</turn_aborted>