Tune paragraph prompts for multilingual noise

config.json

@@ -25,6 +25,9 @@
     "post_correct_min_overlap": 0.45,
     "post_correct_debug": false,
     "llm_paragraph": false,
+    "llm_paragraph_temperature": 0.2,
+    "llm_paragraph_top_p": 0.8,
+    "llm_paragraph_repeat_penalty": 1.0,
     "llm_request_log_path": "logs/llm_requests.jsonl",
     "only_translate_llm": false,
     "temperature_fallback": [0.0, 0.2, 0.4, 0.6, 0.8, 1.0],

engine_llm.py

@@ -31,11 +31,15 @@ Produce the most faithful readable English update.
 Decision rules:
 1. NEW SOURCE TEXT is the primary evidence and should dominate the output.
 2. Use PREVIOUS SOURCE TEXT and PREVIOUS REFINED CONTEXT only when they clearly help resolve or continue the new material.
-3. If the new material starts a fresh thought, output only the new material.
+3. The final answer must be English only.
-4. Remove exact or near-exact repetition unless the repetition is clearly intentional rhetoric.
+4. Never copy non-English words into the output unless they are proper names.
-5. Do not add explanations, disclaimers, or meta commentary.
+5. If a non-English fragment appears as an isolated clause, trailing fragment, or side comment without a clear English anchor, drop it.
-6. Prefer conservative wording over guessed meaning.
+6. Only translate a non-English fragment when it is clearly central to the same idea and its meaning is reasonably obvious from context.
-7. Return only the revised transcript text."""
+7. If the new material starts a fresh thought, output only the new material.
+8. Remove exact or near-exact repetition unless the repetition is clearly intentional rhetoric.
+9. Do not add explanations, disclaimers, or meta commentary.
+10. Prefer conservative wording over guessed meaning.
+11. Return only the revised transcript text."""
 
     prompt = f"""Edit this transcript update.
 
@@ -137,7 +141,10 @@ def run_llm_prompt_test(args):
             raise RuntimeError(f"OpenAI API error {response.status_code}: {detail}")
         return response.json()["choices"][0]["message"]["content"].strip()
 
-    def call_ollama(model, prompt, temperature, system=None):
+    def call_ollama(model, prompt, temperature, system=None, extra_options=None):
+        options = {"temperature": temperature}
+        if extra_options:
+            options.update(extra_options)
         response = requests.post(
             getattr(args, "post_correct_ollama_url", "http://127.0.0.1:11434/api/generate"),
             json={
@@ -145,7 +152,7 @@ def run_llm_prompt_test(args):
                 "prompt": prompt,
                 "system": system or "",
                 "stream": False,
-                "options": {"temperature": temperature},
+                "options": options,
                 "keep_alive": getattr(args, "post_correct_keep_alive", "30m"),
             },
             timeout=getattr(args, "post_correct_llm_timeout", 8.0),
@@ -153,7 +160,7 @@ def run_llm_prompt_test(args):
         response.raise_for_status()
         return response.json().get("response", "").strip()
 
-    def send_test_request(mode, messages, prompt, temperature, metadata, model=None):
+    def send_test_request(mode, messages, prompt, temperature, metadata, model=None, extra_options=None):
         request_model = model or args.post_correct_model
         is_openai = request_model.startswith("gpt-")
         entry = {
@@ -162,6 +169,7 @@ def run_llm_prompt_test(args):
             "provider": "openai" if is_openai else "ollama",
             "model": request_model,
             "temperature": temperature,
+            "options": extra_options or {},
             "messages": messages,
             "metadata": metadata,
         }
@@ -185,7 +193,7 @@ def run_llm_prompt_test(args):
                 system = message.get("content", "")
                 break
         try:
-            response_text = call_ollama(request_model, prompt, temperature, system=system)
+            response_text = call_ollama(request_model, prompt, temperature, system=system, extra_options=extra_options)
             entry["response"] = {"status": "ok", "content": response_text}
             return response_text
         except Exception as exc:
@@ -352,7 +360,10 @@ def run_llm_processor(in_queue, out_queue, args):
             raise RuntimeError(f"OpenAI API error {response.status_code}: {detail}")
         return response.json()["choices"][0]["message"]["content"].strip()
 
-    def call_ollama(prompt, temperature, system=None):
+    def call_ollama(prompt, temperature, system=None, extra_options=None):
+        options = {"temperature": temperature}
+        if extra_options:
+            options.update(extra_options)
         response = requests.post(
             getattr(args, "post_correct_ollama_url", "http://127.0.0.1:11434/api/generate"),
             json={
@@ -360,7 +371,7 @@ def run_llm_processor(in_queue, out_queue, args):
                 "prompt": prompt,
                 "system": system or "",
                 "stream": False,
-                "options": {"temperature": temperature},
+                "options": options,
                 "keep_alive": getattr(args, "post_correct_keep_alive", "30m"),
             },
             timeout=getattr(args, "post_correct_llm_timeout", 8.0),
@@ -368,13 +379,14 @@ def run_llm_processor(in_queue, out_queue, args):
         response.raise_for_status()
         return response.json().get("response", "").strip()
 
-    def call_llm(messages, prompt, temperature, warn_key):
+    def call_llm(messages, prompt, temperature, warn_key, extra_options=None):
         entry = {
             "timestamp": datetime.now(timezone.utc).isoformat(),
             "mode": "paragraph" if warn_key == "paragraph_warned" else "line",
             "provider": "openai" if is_openai else "ollama",
             "model": args.post_correct_model,
             "temperature": temperature,
+            "options": extra_options or {},
             "messages": messages,
         }
         started_at = time.time()
@@ -391,7 +403,7 @@ def run_llm_processor(in_queue, out_queue, args):
                     if message.get("role") == "system":
                         system = message.get("content", "")
                         break
-            response_text = call_ollama(prompt, temperature, system=system)
+            response_text = call_ollama(prompt, temperature, system=system, extra_options=extra_options)
             entry["response"] = {"status": "ok", "content": response_text}
             return response_text
         except Exception as exc:
@@ -412,7 +424,17 @@ def run_llm_processor(in_queue, out_queue, args):
     
     def call_llm_rolling_refine(new_segments_str, context, previous_source_text):
         messages, prompt = build_paragraph_messages(context, previous_source_text, new_segments_str)
-        return call_llm(messages, prompt, 0.1, "paragraph_warned")
+        paragraph_options = {
+            "top_p": getattr(args, "llm_paragraph_top_p", 0.8),
+            "repeat_penalty": getattr(args, "llm_paragraph_repeat_penalty", 1.0),
+        }
+        return call_llm(
+            messages,
+            prompt,
+            getattr(args, "llm_paragraph_temperature", 0.2),
+            "paragraph_warned",
+            extra_options=paragraph_options,
+        )
 
     def post_correct_line(text):
         if not getattr(args, "post_correct", False):

history.md

@@ -297,3 +297,13 @@ The project now features a high-performance, Apple Silicon-optimized pipeline th
     - Identified the best-performing variant as one that treats the new source window as primary evidence while using prior refined and prior raw text only when they clearly continue the thought.
     - Updated `engine_llm.py` so live paragraph refinement now sends `PREVIOUS REFINED CONTEXT`, `PREVIOUS SOURCE TEXT`, and `NEW SOURCE TEXT` with a more conservative, grounding-oriented system prompt.
 - **Outcome:** Paragraph refinement is now substantially better anchored to current source text, with less stale-context carryover and a much lower rate of unsupported content in direct Ollama tests.
+
+## Phase 34: Multilingual Noise Filtering + Paragraph Generation Sweep
+- **Goal:** Make paragraph refinement more robust when stray Spanish or other non-English fragments leak into the English bridge text.
+- **Approach:**
+    - Extended `prompt_experiments.py` with multilingual-noise test cases that inject Spanish, Portuguese, and French fragments into reconstructed live samples.
+    - Ran direct-Ollama comparisons across multiple paragraph prompt variants and generation settings, including temperature, `top_p`, and `repeat_penalty`.
+    - Found that the best overall paragraph generation settings for `qwen2.5:3b-instruct` remained conservative: `temperature=0.2`, `top_p=0.8`, `repeat_penalty=1.0`.
+    - Tightened the live paragraph system prompt in `engine_llm.py` so output must stay English-only, non-English fragments should not be copied through, isolated trailing foreign fragments should be dropped, and only clearly central foreign content should be translated into English.
+    - Added paragraph-specific generation controls to `main_v2.py` and `config.json` so paragraph tuning can evolve independently of line-correction behavior.
+- **Outcome:** Paragraph refinement is now stricter about keeping the final output in English while preserving the more grounded context layout and the best-tested Ollama generation settings.

main_v2.py

@@ -57,6 +57,9 @@ def main():
     parser.add_argument("--post-correct-min-overlap", type=float, default=defaults.get("post_correct_min_overlap", 0.45))
     parser.add_argument("--post-correct-debug", action="store_true", default=defaults.get("post_correct_debug", False))
     parser.add_argument("--llm-paragraph", action="store_true", default=defaults.get("llm_paragraph", False))
+    parser.add_argument("--llm-paragraph-temperature", type=float, default=defaults.get("llm_paragraph_temperature", 0.2))
+    parser.add_argument("--llm-paragraph-top-p", type=float, default=defaults.get("llm_paragraph_top_p", 0.8))
+    parser.add_argument("--llm-paragraph-repeat-penalty", type=float, default=defaults.get("llm_paragraph_repeat_penalty", 1.0))
     parser.add_argument("--llm-request-log-path", type=str, default=defaults.get("llm_request_log_path", "logs/llm_requests.jsonl"))
     parser.add_argument("--llm-test-line", type=str, default=None, help="Send a single line-correction prompt without starting transcription.")
     parser.add_argument("--llm-test-prev1", type=str, default="", help="Optional previous line 1 context for --llm-test-line.")

prompt_experiments.py

@@ -7,6 +7,14 @@ from collections import Counter
 from urllib import request, error
 
 
+FOREIGN_INSERTIONS = [
+    "pero en espanol dice que no retrocedas",
+    "y tambien dijo que Dios responde",
+    "mas ele continuou em fe",
+    "et il a dit de rester ferme",
+]
+
+
 def parse_distribute_debug_log(path):
     entries = []
     current = {"final": None, "en_bridge": None, "llm_paragraph": None}
@@ -65,7 +73,39 @@ def reconstruct_samples(path):
     return samples
 
 
-def build_variants(sample):
+def inject_multilingual_noise(text, sample_index, slot_name):
+    if not text:
+        return text
+    inserts = [
+        FOREIGN_INSERTIONS[(sample_index + 0) % len(FOREIGN_INSERTIONS)],
+        FOREIGN_INSERTIONS[(sample_index + 1) % len(FOREIGN_INSERTIONS)],
+    ]
+    sentences = [part.strip() for part in re.split(r"(?<=[.!?])\s+", text.strip()) if part.strip()]
+    if not sentences:
+        return text
+    if len(sentences) == 1:
+        return f'{sentences[0]} {inserts[0]}.'
+    head = sentences[0]
+    tail = " ".join(sentences[1:])
+    if slot_name == "previous_raw":
+        return f"{head} {inserts[0]}. {tail}"
+    return f"{head} {tail} {inserts[1]}."
+
+
+def maybe_augment_samples(samples, mode):
+    if mode != "multilingual_noise":
+        return samples
+
+    augmented = []
+    for idx, sample in enumerate(samples):
+        updated = dict(sample)
+        updated["previous_raw"] = inject_multilingual_noise(sample["previous_raw"], idx, "previous_raw")
+        updated["new_raw"] = inject_multilingual_noise(sample["new_raw"], idx, "new_raw")
+        augmented.append(updated)
+    return augmented
+
+
+def build_variants(sample, variant_filter=None):
     prev_refined = sample["previous_refined"]
     prev_raw = sample["previous_raw"]
     new_raw = sample["new_raw"]
@@ -198,17 +238,140 @@ Decision rules:
 """
     variants.append(("grounded_new_dominant", system_e, user_e))
 
+    system_g = """You are a careful live transcript editor working from noisy translated source text.
+
+Goal:
+Produce the most faithful readable English update.
+
+Decision rules:
+1. NEW SOURCE TEXT is the primary evidence and should dominate the output.
+2. Use PREVIOUS SOURCE TEXT and PREVIOUS REFINED CONTEXT only when they clearly help resolve or continue the new material.
+3. The final answer must be English only.
+4. If source text contains Spanish, Portuguese, French, or other non-English fragments, translate their meaning into English when reasonably clear.
+5. Never copy non-English words into the output unless they are proper names.
+6. If the new material starts a fresh thought, output only the new material.
+7. Remove exact or near-exact repetition unless the repetition is clearly intentional rhetoric.
+8. Do not add explanations, disclaimers, or meta commentary.
+9. Prefer conservative wording over guessed meaning.
+10. Return only the revised transcript text."""
+    user_g = f"""Edit this transcript update.
+
+[PREVIOUS REFINED CONTEXT]
+"{prev_refined}"
+
+[PREVIOUS SOURCE TEXT]
+"{prev_raw}"
+
+[NEW SOURCE TEXT]
+"{new_raw}"
+"""
+    variants.append(("grounded_english_only_translate", system_g, user_g))
+
+    system_h = """You are a careful live transcript editor working from noisy translated source text.
+
+Goal:
+Produce the most faithful readable English update.
+
+Decision rules:
+1. NEW SOURCE TEXT is the primary evidence and should dominate the output.
+2. Use PREVIOUS SOURCE TEXT and PREVIOUS REFINED CONTEXT only when they clearly help resolve or continue the new material.
+3. The final answer must be English only.
+4. Treat any Spanish, Portuguese, French, or other non-English fragments as contamination from surrounding audio or translation noise unless they clearly repeat the same idea as nearby English.
+5. When a non-English fragment clearly repeats nearby English content, keep only the English meaning once.
+6. Never copy non-English words into the output unless they are proper names.
+7. If the new material starts a fresh thought, output only the new material.
+8. Remove exact or near-exact repetition unless the repetition is clearly intentional rhetoric.
+9. Do not add explanations, disclaimers, or meta commentary.
+10. Prefer conservative wording over guessed meaning.
+11. Return only the revised transcript text."""
+    user_h = f"""Edit this transcript update.
+
+[PREVIOUS REFINED CONTEXT]
+"{prev_refined}"
+
+[PREVIOUS SOURCE TEXT]
+"{prev_raw}"
+
+[NEW SOURCE TEXT]
+"{new_raw}"
+"""
+    variants.append(("grounded_english_only_ignore_noise", system_h, user_h))
+
+    system_i = """You are a careful live transcript editor working from noisy translated source text.
+
+Goal:
+Produce the most faithful readable English update.
+
+Decision rules:
+1. NEW SOURCE TEXT is the primary evidence and should dominate the output.
+2. Use PREVIOUS SOURCE TEXT and PREVIOUS REFINED CONTEXT only when they clearly help resolve or continue the new material.
+3. The final answer must be English only.
+4. Never copy non-English words into the output unless they are proper names.
+5. If a non-English fragment appears as an isolated clause, trailing fragment, or side comment without a clear English anchor, drop it.
+6. Only translate a non-English fragment when it is clearly central to the same idea and its meaning is reasonably obvious from context.
+7. If the new material starts a fresh thought, output only the new material.
+8. Remove exact or near-exact repetition unless the repetition is clearly intentional rhetoric.
+9. Do not add explanations, disclaimers, or meta commentary.
+10. Prefer conservative wording over guessed meaning.
+11. Return only the revised transcript text."""
+    user_i = f"""Edit this transcript update.
+
+[PREVIOUS REFINED CONTEXT]
+"{prev_refined}"
+
+[PREVIOUS SOURCE TEXT]
+"{prev_raw}"
+
+[NEW SOURCE TEXT]
+"{new_raw}"
+"""
+    variants.append(("grounded_english_only_drop_isolated", system_i, user_i))
+
+    system_f = """You are a careful live transcript editor working from noisy translated source text.
+
+Goal:
+Produce the most faithful readable English update.
+
+Decision rules:
+1. NEW SOURCE TEXT is the primary evidence and should dominate the output.
+2. Use PREVIOUS SOURCE TEXT and PREVIOUS REFINED CONTEXT only when they clearly help resolve or continue the new material.
+3. If the new material starts a fresh thought, output only the new material.
+4. Remove exact or near-exact repetition unless the repetition is clearly intentional rhetoric.
+5. If the source repeats the same short clause three or more times in a row, keep at most two repetitions unless losing more would change the speaker's emphasis.
+6. Do not add explanations, disclaimers, or meta commentary.
+7. Prefer conservative wording over guessed meaning.
+8. Return only the revised transcript text."""
+    user_f = f"""Edit this transcript update.
+
+[PREVIOUS REFINED CONTEXT]
+"{prev_refined}"
+
+[PREVIOUS SOURCE TEXT]
+"{prev_raw}"
+
+[NEW SOURCE TEXT]
+"{new_raw}"
+"""
+    variants.append(("grounded_new_dominant_dedupe", system_f, user_f))
+
+    if variant_filter:
+        allowed = set(x.strip() for x in variant_filter.split(",") if x.strip())
+        variants = [variant for variant in variants if variant[0] in allowed]
     return variants
 
 
-def ollama_generate(model, system_prompt, user_prompt, url, timeout, keep_alive):
+def ollama_generate(model, system_prompt, user_prompt, url, timeout, keep_alive, temperature, top_p, repeat_penalty):
     payload = json.dumps(
         {
             "model": model,
             "prompt": user_prompt,
             "system": system_prompt,
             "stream": False,
-            "options": {"temperature": 0.1},
+            "options": {
+                "temperature": temperature,
+                "top_p": top_p,
+                "repeat_penalty": repeat_penalty,
+            },
             "keep_alive": keep_alive,
         }
     ).encode("utf-8")
@@ -258,20 +421,65 @@ def evaluate_output(output, sample):
         "output_len": len(tokenize(output)),
         "repeated_ngram_ratio": round(repeated_ngram_ratio(output), 4),
         "unsupported_token_ratio": round(unsupported_token_ratio(output, evidence), 4),
+        "foreign_leak_ratio": round(foreign_leak_ratio(output), 4),
     }
 
 
+def foreign_leak_ratio(output):
+    foreign_markers = {
+        "pero", "espanol", "tambien", "dijo", "dios", "responde",
+        "mas", "ele", "continuou", "fe",
+        "et", "rester", "ferme",
+    }
+    tokens = tokenize(output)
+    if not tokens:
+        return 0.0
+    leaked = [tok for tok in tokens if tok in foreign_markers]
+    return len(leaked) / len(tokens)
+
+
+def parse_float_grid(raw_value, default_values):
+    if not raw_value:
+        return default_values
+    return [float(x.strip()) for x in raw_value.split(",") if x.strip()]
+
+
+def build_param_sets(args):
+    temperatures = parse_float_grid(args.temperature_grid, [0.1])
+    top_ps = parse_float_grid(args.top_p_grid, [0.9])
+    repeat_penalties = parse_float_grid(args.repeat_penalty_grid, [1.1])
+
+    param_sets = []
+    for temperature in temperatures:
+        for top_p in top_ps:
+            for repeat_penalty in repeat_penalties:
+                label = f"temp={temperature}_top_p={top_p}_repeat_penalty={repeat_penalty}"
+                param_sets.append(
+                    {
+                        "label": label,
+                        "temperature": temperature,
+                        "top_p": top_p,
+                        "repeat_penalty": repeat_penalty,
+                    }
+                )
+    return param_sets
+
+
 def run_experiments(args):
     samples = reconstruct_samples(args.log_file)
+    samples = maybe_augment_samples(samples, args.sample_mode)
     if args.limit:
         samples = samples[: args.limit]
 
+    param_sets = build_param_sets(args)
     results = []
     for index, sample in enumerate(samples):
         print(f"Running sample {index + 1}/{len(samples)}")
         sample_results = []
-        for variant_name, system_prompt, user_prompt in build_variants(sample):
+        for variant_name, system_prompt, user_prompt in build_variants(sample, args.variant_filter):
             print(f"  variant={variant_name}")
+            for params in param_sets:
+                print(f"    params={params['label']}")
                 try:
                     output = ollama_generate(
                         args.model,
@@ -280,6 +488,9 @@ def run_experiments(args):
                         args.ollama_url,
                         args.timeout,
                         args.keep_alive,
+                        params["temperature"],
+                        params["top_p"],
+                        params["repeat_penalty"],
                     )
                     metrics = evaluate_output(output, sample)
                     error_message = None
@@ -290,6 +501,7 @@ def run_experiments(args):
                 sample_results.append(
                     {
                         "variant": variant_name,
+                        "params": params,
                         "output": output,
                         "metrics": metrics,
                         "error": error_message,
@@ -309,9 +521,16 @@ def summarize_results(results):
     summary = {}
     for sample_result in results:
         for variant_result in sample_result["results"]:
+            summary_key = f"{variant_result['variant']}|{variant_result['params']['label']}"
             bucket = summary.setdefault(
-                variant_result["variant"],
+                summary_key,
-                {"repeated_ngram_ratio": [], "unsupported_token_ratio": [], "output_len": [], "errors": 0},
+                {
+                    "repeated_ngram_ratio": [],
+                    "unsupported_token_ratio": [],
+                    "foreign_leak_ratio": [],
+                    "output_len": [],
+                    "errors": 0,
+                },
             )
             if variant_result.get("error"):
                 bucket["errors"] += 1
@@ -336,6 +555,11 @@ def main():
     parser.add_argument("--keep-alive", type=str, default="30m")
     parser.add_argument("--limit", type=int, default=0)
     parser.add_argument("--output-file", type=str, default="logs/prompt_experiment_results.json")
+    parser.add_argument("--variant-filter", type=str, default="", help="Comma-separated subset of variants to run.")
+    parser.add_argument("--temperature-grid", type=str, default="", help="Comma-separated temperatures to test.")
+    parser.add_argument("--top-p-grid", type=str, default="", help="Comma-separated top_p values to test.")
+    parser.add_argument("--repeat-penalty-grid", type=str, default="", help="Comma-separated repeat_penalty values to test.")
+    parser.add_argument("--sample-mode", type=str, default="base", help="Sample mode: base or multilingual_noise.")
     args = parser.parse_args()
 
     results = run_experiments(args)