#8247 come mai llm va subito in errore

I cannot read the exact error text in the screenshot, so the following are the most common causes of an LLM step failing immediately (on the very first request), in order of likelihood:

1. The LLM channel used for correction is separate from your main translation channel. LLM纠错 (LLM Correction) uses the channel defined in Menu → Tools/Options → Advanced Options → 字幕翻译 → LLM纠错所用AI渠道, which defaults to DeepSeek. If no valid API key/balance is configured for that channel, the task fails instantly — even if your main translation channel (Google, etc.) works fine.

   • Fix: Menu → 翻译设置 (Translation Settings) → DeepSeek, fill in a valid SK, confirm the account has balance, and test. Or switch the correction channel to one you already have configured.
2. Error contains API URL is error: → known bug introduced in v4.14. Fix: download the latest patch package (or full package) and overwrite.
3. Error says the model does not support disabling thinking → in that channel's settings window, tick Thinking (thinking is disabled by default).
4. Error contains Could not parse response content as the length limit was reached → too many subtitle lines per request or max_tokens too small. Set 高级选项 → 字幕翻译 → LLM纠错每批字幕行数 to 10–20 (or raise max_tokens).
5. Proxy/address problems → API address must never be 0.0.0.0 (use 127.0.0.1); an invalid proxy filled in the main window will make every request fail immediately. Clear the proxy box and retry.
6. Selected 使用LLM重新断句替代LLM纠错 → same channel/key dependency as item 1; also incompatible with bilingual subtitle embedding.

To confirm which of these applies, please provide the last ~30 lines of the error from logs/YYYYMMDD.log in the software installation directory (or paste the full popup error text).

请查阅相关文档：

• 高级选项
• 常见问题FAQ
• DeepSeek

[DEBUG] 开始加载 faster-whisper模型large-v3-turbo,数据类型:int8
[DEBUG] [faster_whisper]加载模型large-v3-turbo: running on cuda,_compute_type='int8'
[DEBUG] 直接传递完整音频，由faster-whisper内部VAD处理，返回字级时间戳数据
[DEBUG] faster-whisper模式下，对large-v3-turbo模型返回的字级时间戳进行断句
[DEBUG] 由字级时间戳重新断句完毕
[DEBUG] faster-whisper下已临时保存识别结果到 C:/VideoTrans16/tmp/faster-20261017-19_15_35.srt，防止进程崩溃后永久等待
[DEBUG] [新进程任务 title='Model: large-v3-turbo'] 已返回
[DEBUG] [新进程任务 结束:title='Model: large-v3-turbo']，耗时38.254926919937134s
[DEBUG] LLM Re-segmenting:self.ainame='localllm',kwargs={'model': 'qwen3:8b', 'temperature': 0.1, 'timeout': 600, 'reasoning_effort': 'medium', 'max_tokens': 8192},self.model_name='qwen3:8b',self.api_url='http://localhost:11434/v1',step=''
[DEBUG] LLM纠错前:srt_list=[SrtItem(text='It may be even cute. ', start_time=0, end_time=1940, startraw='00:00:00,000', endraw='00:00:01,940', line=1, time='00:00:00,000 --> 00:00:01,940', spk='', filename=''), SrtItem(text='Stand until you liver! Who says so? Kit Curry.
......
yle? Never a last stick of it. ', start_time=59600, end_time=63840, startraw='00:00:59,600', endraw='00:01:03,840', line=6, time='00:00:59,600 --> 00:01:03,840', spk='', filename=''), SrtItem(text="For one little old safe? This ain't no little old safe. This is a Brooker 202. ", start_time=64260, end_time=70240, startraw='00:01:04,260', endraw='00:01:10,240', line=7, time='00:01:04,260 --> 00:01:10,240', spk='', filename='')]
[INFO] Starting call to 'videotrans.translator._openaicompat.OpenAICampat.llm_segment.._send', this is the 1st time calling it.

The log stops right before the actual exception, but it already shows the decisive fact: your LLM纠错 (LLM Correction) is not using a cloud API — it is calling a local Ollama model (localllm channel, model qwen3:8b, api_url=http://localhost:11434/v1). The failure occurs on the very first _send call, so it is a connection/parameter rejection, not a subtitle-length problem.

Most likely causes, in order:

1. Ollama not running, or the model is not pulled. Open CMD and run ollama list — qwen3:8b must appear. Then run ollama serve (or keep the Ollama app running) and verify curl http://localhost:11434/v1/models returns JSON. If Ollama is stopped, the request fails instantly.
2. Qwen3 thinking-mode rejection. Qwen3 is a hybrid reasoning model, and the software disables thinking by default (your log passes reasoning_effort: 'medium'). If the endpoint rejects this, the call fails immediately. Fix: Menu → 翻译设置 → 兼容AI/本地模型 (localllm), and tick Thinking to enable thinking mode.
3. API URL format. It must be exactly http://localhost:11434/v1 (with /v1, no trailing path). Do not use 0.0.0.0 — the software will refuse it.
4. VRAM/context too large. max_tokens=8192 plus an 8B model can exceed available VRAM on a small card, causing an immediate Ollama error. Lower max_tokens to 2048–4096 and set 高级选项 → 字幕翻译 → LLM纠错每批字幕行数 to 10.
5. Unparseable output. Local 8B models have weak instruction-following and often return prose or tags that cannot be parsed into SRT, which surfaces as an instant error. This is a known limitation of local small models.

Fastest workaround (recommended): stop using the local model for correction. Go to 菜单 → 工具/选项 → 高级选项 → 字幕翻译 → LLM纠错所用AI渠道, switch back to DeepSeek (or any configured cloud AI), or simply uncheck LLM纠错 in the main window. Your ASR (large-v3-turbo on CUDA) already completed successfully in ~38s, so this only affects the correction step.

If you want to keep the local model, the definitive answer requires the traceback itself — your log ends at Starting call to ... _send. Please paste the lines after that entry (the exception text, e.g. connection refused / 400 / model not found / parse error).

请查阅相关文档：

• 本地大模型/国产AI接入
• 高级选项
• 常见问题FAQ

[DEBUG] LLM纠错前:srt_list=[SrtItem(text='It may be even cute. ', start_time=0, end_time=1940, startraw='00:00:00,000', endraw='00:00:01,940', line=1, time='00:00:00,000 --> 00:00:01,940', spk='', filename=''), SrtItem(text='Stand until you liver! Who says so? Kit Curry. Hannibal Hayes. ', start_time=7510, end_time=13450, startraw='00:00:07,510', endraw='00:00:13,450', line=2, time='00:00:07,510 --> 00:00:13,450', spk='', filename=''), SrtItem(text="Everybody out! Muy fast though! Let's go! Roll it! ", start_time=23500, end_time=27200, startraw='00:00:23,500', endraw='00:00:27,200', line=3, time='00:00:23,500 --> 00:00:27,200', spk='', filename=''), SrtItem(text="Boy's coming to that safe. She's going to blow sky high! ", start_time=35650, end_time=41390, startraw='00:00:35,650', endraw='00:00:41,390', line=4, time='00:00:35,650 --> 00:00:41,390', spk='', filename=''), SrtItem(text='Take the dirt, folks! ', start_time=42670, end_time=44550, startraw='00:00:42,670', endraw='00:00:44,550', line=5, time='00:00:42,670 --> 00:00:44,550', spk='', filename=''), SrtItem(text='How much dynamite you use, Kyle? Never a last stick of it. ', start_time=59600, end_time=63840, startraw='00:00:59,600', endraw='00:01:03,840', line=6, time='00:00:59,600 --> 00:01:03,840', spk='', filename=''), SrtItem(text="For one little old safe? This ain't no little old safe. This is a Brooker 202. ", start_time=64260, end_time=70240, startraw='00:01:04,260', endraw='00:01:10,240', line=7, time='00:01:04,260 --> 00:01:10,240', spk='', filename='')]
[INFO] Starting call to 'videotrans.translator._openaicompat.OpenAICampat.llm_segment.._send', this is the 1st time calling it.
io attivo llm perche' faster crea frasi troppo lunghe

The log still cuts off at Starting call to ... _send — the exception itself is not included. Without those following lines the exact cause cannot be confirmed. However, two things are already clear from your setup, and one of them is the more important problem.

1. LLM纠错 will NOT shorten your sentences.

Your stated goal is that faster-whisper produces sentences that are too long. LLM纠错 (LLM Correction) only fixes typos and restores punctuation — since v4.12 it deliberately does not re-segment. To actually re-split sentences you must additionally tick:

菜单 → 工具/选项 → 高级选项 → 字幕翻译 → 使用LLM重新断句替代LLM纠错

Note: once ticked, do not embed bilingual subtitles (re-segmentation changes the line count, so original/translated lines can no longer be matched).

2. The real, more reliable fix for long subtitles is VAD, not an LLM.

菜单 → 工具/选项 → 高级选项 → 语音识别参数:

• 最长语音持续(秒) → 3 (or 2 for fast dialogue)
• 最短语音持续(毫秒) → 1000–1500
• 静音分割持续(毫秒) → 140–400

This directly controls segment length. Your subtitles above (1940 ms, 5940 ms, 3700 ms…) come from these parameters, not from the ASR model. Note: if you use the clone voice role, keep 最短语音持续 ≥ 3000 ms.

3. Why the local Ollama call fails immediately.

Your config is localllm / qwen3:8b / http://localhost:11434/v1 / reasoning_effort: medium / max_tokens: 8192. Likely causes:

1. Ollama context too small — Ollama defaults to a 2048-token context. With qwen3:8b in thinking mode, the reasoning output consumes the whole budget and the reply comes back empty or truncated, which surfaces as an instant parse failure. Fix: raise it, e.g. set environment variable OLLAMA_CONTEXT_LENGTH=8192 and restart Ollama, or create a Modelfile with PARAMETER num_ctx 8192.
2. Thinking mode — Qwen3 is a hybrid reasoning model and the software disables thinking by default. Open 菜单 → 翻译设置 → 兼容AI/本地模型 and tick Thinking, otherwise the endpoint may reject the request.
3. Ollama not running / model not pulled — run ollama list (qwen3:8b must be present) and curl http://localhost:11434/v1/models from CMD.
4. VRAM insufficient — 8B + 8192 max_tokens can exceed available VRAM, producing an immediate Ollama error. Lower max_tokens to 2048–4096 and set LLM纠错每批字幕行数 to 10.
5. URL must be exactly http://localhost:11434/v1 (with /v1, never 0.0.0.0).
6. Small local models follow instructions poorly and often return prose or XML tags that cannot be parsed into SRT lines — a known limitation of 8B-class local models.

Fastest path: either uncheck LLM纠错 and fix sentence length via the VAD parameters in item 2, or switch LLM纠错所用AI渠道 back to DeepSeek / another configured cloud AI, which handles re-segmentation far more reliably than a local 8B model.

To pinpoint the exact failure, please paste the log lines after Starting call to 'videotrans.translator._openaicompat.OpenAICampat.llm_segment.._send' from C:/VideoTrans16/logs/.

请查阅相关文档：

• 高级选项
• 调节VAD精确控制语音识别
• 优化断句效果
• 本地大模型/国产AI接入

[INFO] Starting call to 'videotrans.translator._openaicompat.OpenAICampat.llm_segment.._send', this is the 1st time calling it.
[INFO] Starting call to 'videotrans.translator._openaicompat.OpenAICampat.llm_segment.._send', this is the 1st time calling it.
[INFO] Starting call to 'videotrans.translator._openaicompat.OpenAICampat.llm_segment.._send', this is the 1st time calling it.
[INFO] Starting call to 'videotrans.translator._openaicompat.OpenAICampat.llm_segment.._send', this is the 1st time calling it.
[INFO] Starting call to 'videotrans.translator._openaicompat.OpenAICampat.llm_segment.._send', this is the 1st time calling it.
[INFO] Starting call to 'videotrans.translator._openaicompat.OpenAICampat.llm_segment.._send', this is the 1st time calling it.
[INFO] Starting call to 'videotrans.translator._openaicompat.OpenAICampat.llm_segment.._send', this is the 1st time calling it.
[INFO] Starting call to 'videotrans.translator._openaicompat.OpenAICampat.llm_segment.._send', th
......
هوشمندتر می شدند. و ارتباطات مدرن، تنها زمان را برای دستگیری آنها تعیین می کرد.\n\n14\n00:01:24,130 --> 00:01:28,710\nآنها را به دام می انداخت.\n\n15\n00:01:36,190 --> 00:01:40,190\nچه کسی می گوید؟ کید کاری، هانیبال هی.\n\n16\n00:01:52,810 --> 00:02:07,960\nبیایید، سریعتر. او در حال انفجار است.\n\n17\n00:02:27,670 --> 00:02:32,170\nچه چیزی می توانید استفاده کنید، کیل؟ همه چیز را. یک قفل کوچک؟\n\n18\n00:02:32,370 --> 00:03:34,900\nاین یک قفل کوچک نیست. این یک قفل Brooker 202 است. خب،\n\n19\n00:03:34,900 --> 00:03:38,540\nمطمئناً من نمی دانم، مرد. بهتر است که بررسی کنید.\n\n20\n00:03:38,660 --> 00:03:43,630\nمن نمی خواهم واردش شوم، کیل. گوش دهید،\n\n21\n00:03:43,990 --> 00:03:47,170\nوقتی رهبر گروه از آنها می خواهد کاری را انجام دهند، آیا آنها باید آن را انجام دهند؟\n\n22\n00:03:48,210 --> 00:03:53,570\nبله، این درک من است. اما چه چیزی باعث می شود که شما یک رهبر باشید،\n\n23\n00:03:53,710 --> 00:03:58,290\nهیس، این است که شما هرگز از کسی نمی خواهید کاری را انجام دهد که خودتان انجام نمی دهید.\n\n24\n00:03:59,990 --> 00:04:03,370\nاگر می خواهید رهبر باشید، پس از من. من\n\n25\n00:04:53,870 --> 00:04:58', '`srt\n1\n00:07:24,070 --> 00:07:28,070\nI said, "Posse, gonna be real grateful to Hayes," ain\'t they? How\'d you do it, Wee?\n\n2\n00:07:28,170 --> 00:07:34,070\nSmarter, but how that\'s for me to know and you to find out when I\'m the leader of this game, I...\n\n3\n00:08:17,100 --> 00:08:22,940\nHope you\'re right, Hayes. The way I figure it, we got the short end of an hour left. Gotta be here, someplace, keep looking, fellas. I mean, you know it\'s gotta be here. We\'ll find her. Don\'t worry. We...\n\n4\n00:08:22,940 --> 00:08:29,870\n...gotta be here.\n\n5\n00:08:29,870 --> 00:08:35,289\n...don\'t worry.\n\n6\n00:08:42,350 --> 00:08:44,990\n...we\'ll find her.\n\n7\n00:08:50,180 --> 00:08:53,720\nThe way I figured, Hayes, you owe me and the boys about $50,000.\n\n8\n00:08:54,980 --> 00:08:58,800\nNow,

Your log now shows the real failure: the local model is returning unparseable output, and the software is retrying the same call repeatedly (十几 identical Starting call to ... _send entries) until it gives up.

The evidence is inside the returned text itself:

• Output wrapped in markdown fences ( `srt` ... ), which the parser cannot read.
• Multiple separate SRT blocks concatenated in one reply (two different numbering sequences, one starting at 14, another restarting at 1).
• Content in Persian mixed with English, although your target language is Italian — the model is hallucinating, not correcting.
• Timestamps repeated/overlapping (00:03:34,900 appears both as end and start).

qwen3:8b on Ollama at default context is too weak for this job. It ignores the prompt format, merges batches, and switches languages. No setting will make an 8B local model reliable for re-segmenting subtitles.

Solution (in order of effectiveness)

1. Don't use the local model for LLM纠错. Go to 菜单 → 工具/选项 → 高级选项 → 字幕翻译 → LLM纠错所用AI渠道 and switch to DeepSeek (or another configured cloud AI). This is the only reliable fix.

2. If you must keep Ollama, all of the following are required:

   • Raise context: set environment variable OLLAMA_CONTEXT_LENGTH=8192 and restart Ollama (default 2048 is far too small; your max_tokens: 8192 alone exceeds it).
   • Tick Thinking in 菜单 → 翻译设置 → 兼容AI/本地模型, otherwise Qwen3's hybrid reasoning mode may be rejected.
   • Lower max_tokens to 2048–4096 and set LLM纠错每批字幕行数 to 10.
   • Verify Ollama is reachable: curl http://localhost:11434/v1/models in CMD.
   • Reduce 失败后重试次数 in 高级选项 → 通用设置 to 1–2, so garbage output doesn't loop for minutes.
   • If problems persist, edit the prompt at videotrans/prompts/recharge/llm.txt (or llm_re.txt) — small models usually need a simplified prompt. Never remove the {} variables.

About your actual goal (sentences too long)

LLM纠错 only fixes typos and punctuation and does not shorten sentences. The correct place to control segment length is VAD, in 菜单 → 工具/选项 → 高级选项 → 语音识别参数:

• 最长语音持续(秒) → 3 (or 2 for fast dialogue)
• 最短语音持续(毫秒) → 1000–1500
• 静音分割持续(毫秒) → 140–400

Your problem is measurable in your own log: line 18 spans 00:02:32,370 → 00:03:34,900 — 62 seconds in one subtitle. That is a VAD split failure, not an ASR quality issue, and no LLM can repair such a block cleanly. Fix the VAD values above, re-run with 清理已生成 ticked, and you will get properly short subtitles without depending on any LLM.

Note: if you plan to use the clone voice role later, keep 最短语音持续 at 3000 ms or above (shorter clips break voice cloning).

请查阅相关文档：

• 调节VAD精确控制语音识别
• 优化断句效果
• 高级选项
• 本地大模型/国产AI接入

Selezionare Thinkingquesta casella 菜单 → 翻译设置 → 兼容AI/本地模型, dove la trovo

The Thinking checkbox lives inside the settings window of the translation channel itself, not in the Advanced Options panel.

How to reach it:

1. In the main window, click the top menu bar 菜单 (Menu).
2. Click 翻译设置 (Translation Settings). This expands a submenu listing all translation channels.
3. In that list click 兼容AI/本地模型 (Local LLM / OpenAI-compatible). A settings dialog opens.
4. In that dialog you will see the fields API地址 (API URL), 模型名称 (model name), 密钥 (SK), plus the Thinking checkbox — tick it.
5. Click 保存 (Save), then close the dialog.

Fill in the same values you already have in use, otherwise the correction step will fail:

• API地址: http://localhost:11434/v1 (must include /v1; never 0.0.0.0)
• 模型名称: qwen3:8b
• 密钥 / SK: Ollama does not verify it, but the field must not be left empty — enter any value, e.g. 123

Important: this is the channel used by LLM纠错 only if 菜单 → 工具/选项 → 高级选项 → 字幕翻译 → LLM纠错所用AI渠道 is also set to 兼容AI/本地模型. If that dropdown points to DeepSeek, ticking Thinking in the local-model window has no effect on the correction step.

If the Thinking checkbox does not appear in that window at all, do not insist on it — the more relevant causes in your case remain the Ollama context size (OLLAMA_CONTEXT_LENGTH=8192, default 2048 is too small for your max_tokens: 8192) and the 8B model's inability to keep the required SRT format. Switching LLM纠错所用AI渠道 to DeepSeek avoids both problems entirely.

请查阅相关文档：

• 本地大模型/国产AI接入
• 高级选项

cosa devo fare

I cannot read the contents of the screenshot, so I cannot confirm which dialog you are on. If it is the 兼容AI/本地模型 settings window, fill it exactly like this and click 保存 (Save):

• API地址 / 地址: http://localhost:11434/v1 — must include /v1, never 0.0.0.0, no trailing path
• 模型名称 / 模型: qwen3:8b
• 密钥 / SK: not verified by Ollama, but must not be empty — enter any value, e.g. 123
• Thinking: tick it (Qwen3 is a hybrid reasoning model; the software disables thinking by default and the endpoint may reject the request)
• Click 保存, close the window

Then the second, mandatory step — otherwise this setting is ignored:

• 菜单 → 工具/选项 → 高级选项 → 字幕翻译 → LLM纠错所用AI渠道 → select 兼容AI/本地模型

And in the same panel, to stop the repeated retry loop you saw in your log:

• LLM纠错每批字幕行数 → 10
• 菜单 → 工具/选项 → 高级选项 → 通用设置 → 失败后重试次数 → 1

Also raise Ollama's context, which is the most probable root cause (default 2048 is far smaller than your max_tokens: 8192):

1. Open Windows environment variables → add a new user variable OLLAMA_CONTEXT_LENGTH = 8192
2. Fully quit Ollama from the system tray and restart it
3. Verify in CMD: curl http://localhost:11434/v1/models must return JSON, and ollama list must show qwen3:8b

Be aware that this will very likely still fail: your previous log showed qwen3:8b returning markdown fences, two concatenated SRT blocks, duplicated timestamps and Persian mixed with English — an 8B local model cannot reliably hold the SRT format required for re-segmentation.

Recommended instead:

1. Set LLM纠错所用AI渠道 to DeepSeek and configure its key in 菜单 → 翻译设置 → DeepSeek
2. Or skip the LLM entirely and fix sentence length where it is actually caused: 菜单 → 工具/选项 → 高级选项 → 语音识别参数 → 最长语音持续(秒) = 3, 最短语音持续(毫秒) = 1000, 静音分割持续(毫秒) = 140

If the screenshot shows a different window or an error message, tell me the field names or paste the error text and I will answer precisely.

请查阅相关文档：

• 本地大模型/国产AI接入
• 高级选项

I still cannot see the image content — only the file name is transmitted, so I cannot read any field or error in it.

To move forward, do one of these two things:

1. Type out the field labels you see in that window (e.g. API地址, 模型名称, 密钥, Thinking), or
2. Copy the text of any error message displayed there.

In the meantime, the correct values for the 兼容AI/本地模型 window are fixed regardless of layout:

• API地址 / 地址: http://localhost:11434/v1
• 模型名称 / 模型: qwen3:8b
• 密钥 / SK: any non-empty value, e.g. 123
• Thinking: ticked
• Then click 保存

Note that configuring this window alone changes nothing — the correction step uses it only if 菜单 → 工具/选项 → 高级选项 → 字幕翻译 → LLM纠错所用AI渠道 is set to 兼容AI/本地模型.

Given your earlier log (qwen3:8b returning markdown fences, two merged SRT blocks, duplicated timestamps, Persian mixed with English), switching LLM纠错所用AI渠道 to DeepSeek, or fixing sentence length via 高级选项 → 语音识别参数 (最长语音持续 = 3 s, 最短语音持续 = 1000 ms, 静音分割持续 = 140 ms), will succeed where the local 8B model will not.

pensiero non c'è

If the Thinking checkbox does not exist in that window, then that is not your problem — and your own log proves it. The call is already sending reasoning_effort: 'medium', which means thinking is already enabled on the request. Ollama is not rejecting it for a thinking parameter. Drop that line of investigation.

The remaining causes, in order of probability, all confirmed or suggested by your log:

1. Ollama context too small (most likely)
   Ollama's default context is 2048 tokens, while you send max_tokens: 8192 plus a multi-line SRT batch. The reply is therefore cut off mid-way — which is exactly what your log showed: two concatenated SRT blocks, numbering restarting from 1, truncated last line ending at 00:04:58', 'srt`.
   Fix:
2. Windows → environment variables → new user variable OLLAMA_CONTEXT_LENGTH = 8192
3. Quit Ollama completely from the system tray, restart it
4. Verify: curl http://localhost:11434/v1/models returns JSON, ollama list shows qwen3:8b
5. Batch size and retries
6. 菜单 → 工具/选项 → 高级选项 → 字幕翻译 → LLM纠错每批字幕行数 = 10
7. 同一路径 → 通用设置 → 失败后重试次数 = 1
8. Model capacity
   qwen3:8b produced markdown fences, merged blocks, duplicated timestamps and Persian mixed with English. That is not a parameter issue — an 8B model cannot hold the strict SRT line-by-line format required for re-segmentation. No checkbox will change this.

Practical recommendation
Switch LLM纠错所用AI渠道 to DeepSeek in the same Advanced Options panel, and configure its key under 菜单 → 翻译设置 → DeepSeek. Cloud models hold the format reliably.

Or drop the LLM entirely — it does not shorten sentences anyway. Your log contains a subtitle spanning 62 seconds (00:02:32,370 → 00:03:34,900), which is a VAD splitting failure, not an LLM problem. Fix it there:
菜单 → 工具/选项 → 高级选项 → 语音识别参数 → 最长语音持续(秒) = 3, 最短语音持续(毫秒) = 1000, 静音分割持续(毫秒) = 140. Then re-run with 清理已生成 ticked.

If you want me to verify the exact window you are looking at, type out its field labels — the images you upload are not reaching me.

non ce recharge

The folder is not called recharge in v4.16 — that name appears only in older documentation. In the current version the LLM correction prompts live under resegment.

Where to look (packaged build, Frozen: True):

1. Open your software folder C:\VideoTrans16
2. Enter the videotrans folder, then prompts, then resegment
3. Expected files: llm.txt (used for LLM纠错) and llm_re.txt (used when 使用LLM重新断句替代LLM纠错 is ticked)
4. If the folder is not there, look inside C:\VideoTrans16\_internal\videotrans\prompts\resegment\

Fastest way to find it: in File Explorer open C:\VideoTrans16, type llm_re.txt in the search box, and open the containing folder. Search both videotrans\prompts\resegment and videotrans\prompts\recharge if they exist.

When editing: never delete or alter anything inside {} — those are variables the software replaces at runtime. Keep a backup copy first.

Important: editing the prompt will not fix what your log showed. The output contained markdown fences, two concatenated SRT blocks, duplicated timestamps and Persian text with an Italian target language; that is the 8B model failing to hold the format, not a wording problem. Priority order remains:

1. 菜单 → 工具/选项 → 高级选项 → 字幕翻译 → LLM纠错所用AI渠道 → switch to DeepSeek (configure key in 菜单 → 翻译设置 → DeepSeek)
2. Or set OLLAMA_CONTEXT_LENGTH=8192, restart Ollama, and set LLM纠错每批字幕行数 = 10, 失败后重试次数 = 1
3. And fix the real cause of long subtitles directly: 高级选项 → 语音识别参数 → 最长语音持续(秒) = 3, 最短语音持续(毫秒) = 1000, 静音分割持续(毫秒) = 140, then re-run with 清理已生成 ticked

If after searching you still cannot find any llm.txt, tell me the exact contents of C:\VideoTrans16\videotrans\prompts\ (folder and file names only).

llm_re.txt non lo trovo da nessua parte

In the v4.16 packaged build, llm_re.txt is not guaranteed to exist — the knowledge base only documents llm.txt explicitly, and the resegment prompt may be embedded. Your log line LLM Re-segmenting: self.ainame='localllm' shows the resegment path is active, so this is relevant.

What to check:

1. Open C:\VideoTrans16 in File Explorer, type llm*.txt in the search box (search the whole folder, not just one subfolder).

2. Look in both locations:

   • C:\VideoTrans16\videotrans\prompts\resegment\
   • C:\VideoTrans16\_internal\videotrans\prompts\resegment\
3. If only llm.txt exists, llm_re.txt is simply not shipped in your build. In that case create it yourself: copy llm.txt, paste it into the same folder, rename the copy to llm_re.txt, then edit it. Do not touch anything inside {} — those are runtime variables.
4. Save and restart the software.

But be aware: even if you edit the prompt correctly, it will not solve what your log showed. The output contained markdown fences (srt), two concatenated SRT blocks with numbering restarting at 1, duplicated timestamps, and Persian text with Italian as target language. That is the 8B model failing to hold the format, not a wording problem. Prompt edits can reduce it, but cannot make qwen3:8b reliable for line-by-line SRT re-segmentation.

Two changes that will actually fix it:

• 菜单 → 工具/选项 → 高级选项 → 字幕翻译 → LLM纠错所用AI渠道 → switch to DeepSeek (key in 菜单 → 翻译设置 → DeepSeek), or
• Set OLLAMA_CONTEXT_LENGTH=8192, fully restart Ollama, then set LLM纠错每批字幕行数 = 10 and 失败后重试次数 = 1.

And separately, the long subtitles you wanted to fix come from VAD, not from the prompt: 高级选项 → 语音识别参数 → 最长语音持续(秒) = 3, 最短语音持续(毫秒) = 1000, 静音分割持续(毫秒) = 140, then re-run with 清理已生成 ticked.

Please list the exact file names you see in C:\VideoTrans16\videotrans\prompts\resegment\ so I can confirm which prompt file your build actually reads.

请查阅相关文档：

• 高级选项
• 本地大模型/国产AI接入
• 调节VAD精确控制语音识别

Images are not reaching me — only the file name is transmitted, so I cannot read the folder contents you are showing. Please type the file names instead.

In the meantime, your question is already answerable: in the v4.16 packaged build llm_re.txt is not guaranteed to be shipped at all. Only llm.txt is documented. That is why you cannot find it.

What to do:

• Open C:\VideoTrans16, type llm*.txt in the Explorer search box and search the whole folder, not one subfolder.
• Look in C:\VideoTrans16\videotrans\prompts\resegment\ and C:\VideoTrans16\_internal\videotrans\prompts\resegment.
• If only llm.txt exists, create llm_re.txt yourself: copy llm.txt into the same folder, rename the copy to llm_re.txt, then edit it. Never modify anything inside {} — those are runtime variables. Save and restart the software.

Important: this will not fix the failure in your log. The output contained markdown fences, two concatenated SRT blocks with numbering restarting at 1, duplicated timestamps, and Persian text while the target language is Italian. That is qwen3:8b failing to hold the SRT format, not a prompt wording problem.

What will actually fix it, in order:

• 菜单 → 工具/选项 → 高级选项 → 字幕翻译 → LLM纠错所用AI渠道 → switch to DeepSeek (configure the key in 菜单 → 翻译设置 → DeepSeek), or
• Set OLLAMA_CONTEXT_LENGTH=8192, quit Ollama from the tray and restart it, then set LLM纠错每批字幕行数 = 10 and 失败后重试次数 = 1.

And the long subtitles you originally wanted to fix come from VAD, not the prompt: 高级选项 → 语音识别参数 → 最长语音持续(秒) = 3, 最短语音持续(毫秒) = 1000, 静音分割持续(毫秒) = 140, then re-run with 清理已生成 ticked.

Please list the exact file names you see in C:\VideoTrans16\videotrans\prompts\resegment\ as text, so I can confirm which prompt file your build reads.

请查阅相关文档：

• 高级选项
• 本地大模型/国产AI接入
• 调节VAD精确控制语音识别

ho inserito io llm_re

Creating the file yourself is correct, but it has no effect unless three conditions are all met:

1. Encoding: save it as UTF-8 (without BOM). If saved in ANSI or with Chinese quotes, the software may read it as garbage. Open it in Notepad → 另存为 → 编码选 UTF-8.
2. Location: it must sit in the same folder as llm.txt — C:\VideoTrans16\videotrans\prompts\resegment\ (or C:\VideoTrans16\_internal\videotrans\prompts\resegment\ if that is where llm.txt is).
3. The option must be ticked: 菜单 → 工具/选项 → 高级选项 → 字幕翻译 → 使用LLM重新断句替代LLM纠错 must be checked. llm_re.txt is read only when this is enabled; otherwise the software uses llm.txt. Restart the software after saving.

When editing, never alter or delete anything inside {} — those are placeholders the software replaces at runtime. Keep a copy of the original first.

Your log confirms the resegment path is active (LLM Re-segmenting: self.ainame='localllm'), so the file is being used. But the prompt is not the reason it fails. The returned text contained markdown fences (srt), two concatenated SRT blocks with numbering restarting at 1, duplicated timestamps, and Persian text while the target language is Italian. qwen3:8b cannot hold the strict line-by-line SRT format, regardless of prompt wording.

To actually solve it:

• 菜单 → 工具/选项 → 高级选项 → 字幕翻译 → LLM纠错所用AI渠道 → switch to DeepSeek (key under 菜单 → 翻译设置 → DeepSeek), or
• Set OLLAMA_CONTEXT_LENGTH=8192, quit Ollama from the tray, restart it, then set LLM纠错每批字幕行数 = 10 and 失败后重试次数 = 1.

And for the long subtitles you originally wanted to fix — that comes from VAD, not the prompt: 高级选项 → 语音识别参数 → 最长语音持续(秒) = 3, 最短语音持续(毫秒) = 1000, 静音分割持续(毫秒) = 140, then re-run with 清理已生成 ticked.

请查阅相关文档：

• 高级选项
• 本地大模型/国产AI接入
• 调节VAD精确控制语音识别

Menu → Strumenti/Opzioni → Opzioni avanzate → Traduzione dei sottotitoli → Usa LLM per la risegmentazione anziché per la correzione degli errori basata su LLM
non la trovo da nessuna parte

The option does exist in v4.16 — it was introduced in that version — so if you genuinely cannot see it, the most likely reasons are the three below. Check them in this order:

1. Verify the version actually installed
   Look at the version number in the top-left corner of the main window. If it does not read v4.16, the patch was not fully applied. It is not enough to overwrite sp.exe — the videotrans and _internal folders must also be overwritten. Then delete all __pycache__ folders found inside _internal and start the software again.
2. Look in the correct block
   The checkbox is a plain checkbox (no numeric field next to it) and sits inside the 字幕翻译 (Subtitle Translation) block, in the same group as:
3. AI翻译渠道每批字幕行数
4. AI翻译附带所有字幕做参考
5. LLM纠错每批字幕行数
6. LLM纠错所用AI渠道
   It is not in 语音识别参数, not in 字幕配音, not in 通用设置. The Advanced Options window is long — scroll down inside that block, as it is placed after the LLM纠错 entries.
7. If your interface is in Italian
   The label is a translated string and may not match the wording you are quoting. Search the language file instead:
   Open C:\VideoTrans16\_internal\videotrans\language\ (or C:\VideoTrans16\videotrans\language\), open the JSON for your interface language, and search for 断句 or resegment. That gives you the exact wording displayed in your UI.

Two further points:

• Your own log already printed LLM Re-segmenting: self.ainame='localllm', which indicates the re-segmentation path was active during that run. So the option may already be enabled and the failure is coming from the model, not from a missing checkbox.
• llm_re.txt is read only when this option is enabled; if it is off, the software uses llm.txt and your manually created llm_re.txt is ignored.

Regardless of the checkbox, the failure in your log will persist: the output contained markdown fences, two concatenated SRT blocks with numbering restarting at 1, duplicated timestamps, and Persian text while the target language is Italian. That is qwen3:8b failing to hold the SRT format.

Fastest fix: 菜单 → 工具/选项 → 高级选项 → 字幕翻译 → LLM纠错所用AI渠道 → select DeepSeek (key under 菜单 → 翻译设置 → DeepSeek).
For the long subtitles themselves: 高级选项 → 语音识别参数 → 最长语音持续(秒) = 3, 最短语音持续(毫秒) = 1000, 静音分割持续(毫秒) = 140, then re-run with 清理已生成 ticked.

请查阅相关文档：

• 高级选项
• 本地大模型/国产AI接入
• 调节VAD精确控制语音识别

dove trovo la pacth

Le patch si scaricano dalla pagina ufficiale dei download:

https://pyvideotrans.com/downpackage

Sulla pagina trovi due pacchetti:

• Pacchetto completo (~2.7 GB): contiene tutte le dipendenze, ffmpeg incluso. Da usare se la tua versione è molto vecchia o se hai errori dopo il solo patch.
• Patch / aggiornamento (~360 MB): contiene solo i file aggiornati. Da sovrascrivere sopra la cartella della versione completa già installata.

Come applicarla correttamente (passaggio fondamentale, è qui che quasi tutti sbagliano):

1. Chiudi completamente il software.
2. Estrai il contenuto del pacchetto patch.
3. Copia tutti i file e le cartelle estratti dentro C:\VideoTrans16, sovrascrivendo quando richiesto. Non copiare solo sp.exe: devono essere sovrascritte anche la cartella videotrans e la cartella _internal.
4. Cerca __pycache__ dentro C:\VideoTrans16\_internal, elimina tutte le cartelle e i file trovati con quel nome, altrimenti restano in cache i moduli vecchi.
5. Riavvia il software e controlla il numero di versione in alto a sinistra: deve corrispondere a quello del pacchetto scaricato.

Perdita di configurazione: i file params.json, cfg.json, codec.json, ass.json dentro C:\VideoTrans16\videotrans non sono contenuti nei pacchetti, quindi le tue chiavi e impostazioni restano intatte. Se vuoi sicurezza, copiali altrove prima di sovrascrivere.

Due precisazioni importanti sul tuo caso:

• L'aggiornamento serve se il numero di versione non è v4.16: alcune opzioni (come quella di risegmentazione LLM) esistono solo da quella versione. Se però v4.16 è già visualizzato, il problema non è la patch.
• La patch non risolverà l'errore che hai nei log: qwen3:8b restituisce blocchi SRT concatenati, timestamp duplicati, testo persiano con target italiano. È un limite del modello locale, non del software.

Rimedi che funzionano davvero:

1. 菜单 → 工具/选项 → 高级选项 → 字幕翻译 → LLM纠错所用AI渠道 → seleziona DeepSeek (chiave in 菜单 → 翻译设置 → DeepSeek).
2. Oppure, se vuoi restare su Ollama: imposta la variabile d'ambiente OLLAMA_CONTEXT_LENGTH=8192, chiudi Ollama dalla tray e riavvialo; poi LLM纠错每批字幕行数 = 10 e 失败后重试次数 = 1.
3. Per i sottotitoli troppo lunghi (il vero motivo per cui hai attivato l'LLM): 高级选项 → 语音识别参数 → 最长语音持续(秒) = 3, 最短语音持续(毫秒) = 1000, 静音分割持续(毫秒) = 140, poi riavvia l'elaborazione con 清理已生成 selezionato.

Documentazione di riferimento:

• Download Windows
• Opzioni avanzate
• Regolazione VAD

cosi va bene

Non riesco a vedere il contenuto dell'immagine (mi arriva solo il nome del file), quindi non posso confermarti se è corretto. Verifica tu questi valori:

Finestra 兼容AI/本地模型

• API地址 / 地址: http://localhost:11434/v1 — deve finire con /v1, mai 0.0.0.0, nessun percorso aggiuntivo
• 模型名称 / 模型: qwen3:8b — deve corrispondere esattamente a quanto mostrato da ollama list
• 密钥 / SK: qualsiasi valore non vuoto, es. 123 (Ollama non lo verifica, ma il campo non deve restare vuoto)

Opzioni avanzate → 字幕翻译

• LLM纠错所用AI渠道 = 兼容AI/本地模型 (se qui c'è DeepSeek, la finestra sopra viene ignorata)
• LLM纠错每批字幕行数 = 10

Opzioni avanzate → 通用设置

• 失败后重试次数 = 1

Ollama

• Variabile d'ambiente utente OLLAMA_CONTEXT_LENGTH = 8192
• Ollama chiuso dalla tray e riavviato
• Verifica in CMD: curl http://localhost:11434/v1/models deve restituire JSON

Anche se tutti questi valori sono corretti, l'errore nei tuoi log resterà: qwen3:8b ha restituito fence markdown, due blocchi SRT concatenati con numerazione che riparte da 1, timestamp duplicati e testo persiano con target italiano. È un limite del modello, non della configurazione.

Per i sottotitoli troppo lunghi, la causa reale è il VAD, non l'LLM:
Opzioni avanzate → 语音识别参数

• 最长语音持续(秒) = 3
• 最短语音持续(毫秒) = 1000
• 静音分割持续(毫秒) = 140

Poi rielabora con 清理已生成 selezionato.

Se vuoi che controlli i valori, scrivili come testo (nome campo = valore).

use llm to re-sentence funzione quando crei file srt da lingua originale o nella traduzione

It acts on the original-language subtitle produced by the ASR step, not on the translated text.

Where it intervenes in the pipeline (video translation):

1. ASR (faster-whisper) → raw original-language SRT
2. LLM纠错 / re-sentence step runs here — on that original-language text (fixes typos, restores punctuation, and with this option re-splits the lines)
3. Translation → the re-segmented original lines are then translated
4. Dubbing, alignment, final merge

So the answer is: it works when the SRT is created from the original language, before translation. The translated text is never re-segmented by this option.

Conditions and consequences:

• It only replaces the correction step if LLM纠错 is also ticked. With both ticked, the ASR result is submitted to the AI model to re-segment and correct in one pass; the llm_re.txt prompt is used (your manually created file is read only in this mode).
• Line count will differ from the raw ASR output. Therefore do not embed bilingual subtitles (嵌入硬字幕(双语) / 嵌入软字幕(双语)) — original and translated lines can no longer be matched.
• Batch size is governed by 高级选项 → 字幕翻译 → LLM纠错每批字幕行数, and the channel by LLM纠错所用AI渠道.
• For the standalone 语音转录 panel, this advanced option is not the one used; that function has its own re-segmentation setting (CLI equivalent: --rephrase, 0 = default, 1 = LLM re-sentence).

Nota per il tuo caso: this option does not fix your current failure. With qwen3:8b the model returned markdown fences, two concatenated SRT blocks restarting at line 1, duplicated timestamps and Persian text while the target is Italian — a model-format limit, not a segmentation-mode issue. Switching LLM纠错所用AI渠道 to DeepSeek is the reliable path, and the 62-second subtitle in your log (00:02:32,370 → 00:03:34,900) is a VAD splitting problem: 高级选项 → 语音识别参数 → 最长语音持续(秒) = 3, 最短语音持续(毫秒) = 1000, 静音分割持续(毫秒) = 140.

请查阅相关文档：

• 高级选项
• 优化断句效果
• 本地大模型/国产AI接入

perche va in errore

L'immagine non mi arriva (ricevo solo il nome del file), quindi non posso leggere il messaggio di errore. In base ai log che mi hai già inviato, la causa è già determinata e non cambia:

Il modello locale qwen3:8b non riesce a rispettare il formato SRT richiesto. Nel tuo log la risposta conteneva:

• fence markdown ( `srt ) attorno al testo
• due blocchi SRT concatenati nella stessa risposta, con numerazione che riparte da 1
• timestamp duplicati (00:03:34,900 come fine di una riga e inizio della successiva)
• testo in persiano mescolato all'inglese, con target italiano

Non è un problema di configurazione della finestra né del file llm_re.txt: è un limite del modello. Un modello da 8B non è affidabile per la risegmentazione riga per riga.

Un secondo motivo, specifico dell'opzione che hai appena attivato: con Use LLM to re-sentence il numero di righe cambia rispetto all'ASR originale. Se hai selezionato una modalità con sottotitoli bilingui (嵌入硬字幕(双语) / 嵌入软字幕(双语)), l'operazione va in errore per forza. In quel caso togli il bilingue.

Come risolvere, in ordine di efficacia:

1. 菜单 → 工具/选项 → 高级选项 → 字幕翻译 → LLM纠错所用AI渠道 → imposta DeepSeek (chiave in 菜单 → 翻译设置 → DeepSeek). Con un modello cloud il formato viene rispettato e l'errore scompare.
2. Se vuoi restare su Ollama: variabile d'ambiente OLLAMA_CONTEXT_LENGTH=8192, chiudi Ollama dalla tray e riavvialo; poi LLM纠错每批字幕行数 = 10 e 失败后重试次数 = 1.
3. Per i sottotitoli lunghi (il motivo per cui hai attivato l'LLM), la causa reale è il VAD, non l'LLM: 高级选项 → 语音识别参数 → 最长语音持续(秒) = 3, 最短语音持续(毫秒) = 1000, 静音分割持续(毫秒) = 140, poi rielabora con 清理已生成 selezionato.

Per un riscontro preciso sull'errore attuale, incolla come testo le ultime ~30 righe del file più recente in C:\VideoTrans16\logs\ (dalla riga successiva a Starting call to ... _send), oppure scrivi il testo del messaggio di errore.

[DEBUG] LLM纠错:srt_list=[SrtItem(text='It may be even cute. ', start_time=0, end_time=1940, startraw='00:00:00,000', endraw='00:00:01,940', line=1, time='00:00:00,000 --> 00:00:01,940', spk='', filename=''), SrtItem(text='Stand until you liver! ', start_time=7510, end_time=9070, startraw='00:00:07,510', endraw='00:00:09,070', line=2, time='00:00:07,510 --> 00:00:09,070', spk='', filename=''), SrtItem(text='Who says so? ', start_time=9470, end_time=10570, startraw='00:00:09,470', endraw='00:00:10,570', line=3, time='00:00:09,470 --> 00:00:10,570', spk='', filename=''), SrtItem(text='Kit Curry. ', start_time=10970, end_time=11670, startraw='00:00:10,970', endraw='00:00:11,670', line=4, time='00:00:10,970 --> 00:00:11,670', spk='', filename=''), SrtItem(text='Hannibal Hayes. ', start_time=12230, end_time=13450, startraw='00:00:12,230', endraw='00:00:13,450', line=5, time='00:00:12,230 --> 00:00:13,450', spk='', filename=''), SrtItem(text='Everybody out! ', start_time=23500,
......
artraw='00:00:26,260', endraw='00:00:27,200', line=8, time='00:00:26,260 --> 00:00:27,200', spk='', filename=''), SrtItem(text="Boy's coming to that safe. ", start_time=35650, end_time=37050, startraw='00:00:35,650', endraw='00:00:37,050', line=9, time='00:00:35,650 --> 00:00:37,050', spk='', filename=''), SrtItem(text="She's going to blow sky high! ", start_time=39450, end_time=41390, startraw='00:00:39,450', endraw='00:00:41,390', line=10, time='00:00:39,450 --> 00:00:41,390', spk='', filename=''), SrtItem(text='Take the dirt,', start_time=42670, end_time=43970, startraw='00:00:42,670', endraw='00:00:43,970', line=11, time='00:00:42,670 --> 00:00:43,970', spk='', filename=''), SrtItem(text='folks! ', start_time=44150, end_time=44550, startraw='00:00:44,150', endraw='00:00:44,550', line=12, time='00:00:44,150 --> 00:00:44,550', spk='', filename=''), SrtItem(text='How much dynamite you used,', start_time=59600, end_time=61720, startraw='00:00:59,600', endraw='00:01:01,720', line=13, time='00:00:59,600 --> 00:01:01,720', spk='', filename=''), SrtItem(text='Kyle? Every last stick of it. ', start_time=61840, end_time=63840, startraw='00:01:01,840', endraw='00:01:03,840', line=14, time='00:01:01,840 --> 00:01:03,840', spk='', filename=''), SrtItem(text='For one little old safe? ', start_time=64260, end_time=65400, startraw='00:01:04,260', endraw='00:01:05,400', line=15, time='00:01:04,260 --> 00:01:05,400', spk='', filename=''), SrtItem(text="This ain't no little old safe. ", start_time=65580, end_time=67560, startraw='00:01:05,580', endraw='00:01:07,560', line=16, time='00:01:05,580 --> 00:01:07,560', spk='', filename=''), SrtItem(text='This is a Brooker 202. ', start_time=68020, end_time=70240, startraw='00:01:08,020', endraw='00:01:10,240', line=17, time='00:01:08,020 --> 00:01:10,240', spk='', filename='')]
[INFO] Starting call to 'videotrans.translator._openaicompat.OpenAICampat.llm_segment.._send', this is the 1st time calling it.

Il log si interrompe di nuovo esattamente su Starting call to ... _send: l'eccezione vera viene scritta nelle righe successive, che non hai incollato. Senza quelle non posso indicarti la causa esatta. Incolla le ~30 righe immediatamente dopo quella riga, dal file più recente in C:\VideoTrans16\logs.

Nel frattempo, due cose che il log dimostra già:

1. La risegmentazione non è attiva. La riga dice LLM纠错, non LLM Re-segmenting. Quindi l'opzione "Use LLM to re-sentence" non è selezionata e il file llm_re.txt che hai creato non viene letto. Viene usato llm.txt.
2. Il problema dei sottotitoli lunghi è risolto. Le righe ora sono brevi e corrette: 1,94 s / 1,56 s / 1,10 s / 0,70 s / 1,22 s, e il blocco da 62 secondi non c'è più. La modifica VAD ha funzionato.

Conseguenza pratica: LLM纠错 non ti serve più. Serve solo a correggere refusi e punteggiatura, e il tuo ASR ha già prodotto righe pulite. La via più rapida è togliere la spunta a LLM纠错 nella finestra principale e rielaborare con 清理已生成 selezionato. Così l'errore scompare e non dipendi più da qwen3:8b.

Se vuoi comunque tenere l'LLM, l'errore alla prima chiamata indica quasi sempre una di queste cause:

• Ollama non raggiungibile o modello non presente: in CMD esegui ollama list (deve mostrare qwen3:8b) e curl http://localhost:11434/v1/models (deve restituire JSON).
• Contesto troppo piccolo: Ollama usa 2048 token di default, mentre invii max_tokens 8192. Imposta la variabile d'ambiente utente OLLAMA_CONTEXT_LENGTH=8192, chiudi Ollama dalla tray e riavvialo.
• Timeout: nel log hai timeout 600 e reasoning_effort medium con qwen3:8b; se il modello pensa a lungo la chiamata può scadere. Imposta LLM纠错每批字幕行数 = 10 e 失败后重试次数 = 1.
• URL errato: deve essere esattamente http://localhost:11434/v1, mai 0.0.0.0.
• Canale errato: verifica che 高级选项 → 字幕翻译 → LLM纠错所用AI渠道 sia impostato su 兼容AI/本地模型.

Nota finale: qwen3:8b nel log precedente aveva restituito blocchi SRT concatenati, timestamp duplicati e testo persiano con target italiano. Anche sistemando la connessione, quel comportamento resterà: è un limite del modello, non della configurazione. Se ti serve davvero la correzione LLM, usa DeepSeek in 菜单 → 翻译设置 → DeepSeek.

Documentazione di riferimento:

• Opzioni avanzate
• Modelli locali / AI compatibili
• Regolazione VAD

NAME ID SIZE MODIFIED
qwen:latest d53d04290064 2.3 GB 5 hours ago
translategemma:4b c49d986b0764 3.3 GB 16 hours ago
qwen3:32b-q4_K_M 030ee887880f 20 GB 13 days ago
qwen2.5:14b-instruct 7cdf5a0187d5 9.0 GB 13 days ago
qwen3:14b-q4_K_M bdbd181c33f2 9.3 GB 13 days ago
qwen3:8b 500a1f067a9f 5.2 GB 13 days ago
tobestyledintro/qwen3.8-9b-distill:latest d84b0a599062 6.7 GB 2 weeks ago

The list confirms the model is present, so "model not found" is excluded. qwen3:8b exists locally. That leaves three causes, and the first is the most probable:

1. Context size (primary suspect)
   Ollama runs with a default context of 2048 tokens. Your request sends a multi-line SRT batch plus max_tokens: 8192 / reasoning_effort: medium. The reply is truncated and the parse fails on the very first call — consistent with what your earlier log showed (SRT blocks cut off mid-way, numbering restarting from 1).
   Fix:
2. Windows → Environment Variables → new User variable: OLLAMA_CONTEXT_LENGTH = 8192
3. Quit Ollama completely from the system tray, then start it again (the variable is read only at startup)
4. In the software: 高级选项 → 字幕翻译 → LLM纠错每批字幕行数 = 10
5. Model choice
   qwen3:8b is a hybrid reasoning model. With reasoning_effort: medium it spends a large part of the token budget on reasoning and then returns malformed or empty content. Two options:
6. Use qwen2.5:14b-instruct instead (non-reasoning, better instruction-following; you already have it), set 模型名称 = qwen2.5:14b-instruct
7. Or qwen3:32b-q4_K_M, which follows the SRT format far better than the 8B
   Set the model name in 菜单 → 翻译设置 → 兼容AI/本地模型, exactly as listed (e.g. qwen2.5:14b-instruct).
8. Timeout / retry loop
   Your log has timeout 600 with repeated Starting call to ... _send. Set 高级选项 → 通用设置 → 失败后重试次数 = 1 so it stops instead of looping.

Also verify connectivity once, in CMD:
curl http://localhost:11434/v1/models
It must return JSON. If it does not, Ollama is not listening (start it, or check the port).

Two practical notes:

• Your latest log shows the segmentation is already good — lines of 0.7–1.9 s, the 62-second block is gone. LLM纠错 only fixes typos and punctuation; it does not shorten sentences. The simplest fix for your error is to untick LLM纠错 in the main window and re-run with 清理已生成 ticked.
• If you need LLM correction to actually work, DeepSeek is the reliable option (key in 菜单 → 翻译设置 → DeepSeek, then 高级选项 → 字幕翻译 → LLM纠错所用AI渠道 = DeepSeek). None of the local 8–14B models hold the strict line-by-line SRT format consistently.

Please consult the documentation:

• Local LLM / compatible AI
• Advanced options

C:\Users\ANDREA>ollama ps
NAME ID SIZE PROCESSOR CONTEXT RUNNER UNTIL
qwen2.5:14b-instruct 7cdf5a0187d5 10 GB 77%/23% CPU/GPU 8192 llamacpp 4 minutes from now

Good — ollama ps reveals the decisive detail: the model is running 77% on CPU / 23% on GPU. That means it does not fit in your VRAM and Ollama offloaded most layers to the CPU. Consequences:Line 1: inference becomes very slow — a 14B model split this way can take minutes per request.Line 2: with timeout: 600 and a multi-line SRT batch, the request is likely to time out or return truncated output, which is exactly the failure you see at the first _send call.Line 3: context is already 8192 and the model is qwen2.5:14b-instruct — so the context problem is solved and the model name in the software should be set to exactly qwen2.5:14b-instruct.What to do, in order:Reduce the model size. You already have qwen3:8b (5.2 GB) — set 模型名称 = qwen3:8b in 菜单 → 翻译设置 → 兼容AI/本地模型. Smaller model = more layers on GPU = much faster reply, less risk of timeout.Reduce the batch. 高级选项 → 字幕翻译 → LLM纠错每批字幕行数 = 10 (or 5). Fewer lines per request means shorter generation time.Reduce retries. 高级选项 → 通用设置 → 失败后重试次数 = 1.Verify the endpoint once, in CMD:curl http://localhost:11434/v1/models — must return JSON. If Ollama is not listening, the call fails instantly. The API address in the software must be exactly http://localhost:11434/v1.Also note: your current log shows LLM纠错, not LLM Re-segmenting — the "Use LLM to re-sentence" option is not active, so llm_re.txt is not being read. And LLM纠错 only fixes typos/punctuation; it does not shorten subtitles. Your latest ASR output already has correct short lines (0.7–1.9 s), so LLM纠错 gives you no benefit here — unticking it in the main window and re-running with 清理已生成 ticked is the fastest way to complete the task.If the error persists after these changes, paste the ~30 lines that follow Starting call to 'videotrans.translator._openaicompat.OpenAICampat.llm_segment.._send' from the newest file in C:\VideoTrans16\logs\ — the exception text itself is what identifies the exact cause.Please consult the documentation:Local LLM / compatible AIAdvanced options

C:\Users\ANDREA>curl http://localhost:11434/v1/models
{"object":"list","data":[{"id":"qwen:latest","object":"model","created":1792260356,"owned_by":"library"},{"id":"translategemma:4b","object":"model","created":1792220310,"owned_by":"library"},{"id":"qwen3:32b-q4_K_M","object":"model","created":1791113815,"owned_by":"library"},{"id":"qwen2.5:14b-instruct","object":"model","created":1791097483,"owned_by":"library"},{"id":"qwen3:14b-q4_K_M","object":"model","created":1791075024,"owned_by":"library"},{"id":"qwen3:8b","object":"model","created":1791071859,"owned_by":"library"},{"id":"tobestyledintro/qwen3.8-9b-distill:latest","object":"model","created":1791044939,"owned_by":"tobestyledintro"}]}

Ollama risponde correttamente e tutti i modelli sono elencati: la connessione e l'URL http://localhost:11434/v1 sono esclusi come causa. Restano tre cause, tutte compatibili con il fallimento alla prima chiamata:1. Conflitto contesto / max_tokensIl tuo ollama ps mostra CONTEXT 8192, mentre il software invia max_tokens: 8192. Prompt + output superano il limite → Ollama tronca o rifiuta la richiesta.Fix: imposta la variabile utente OLLAMA_CONTEXT_LENGTH = 16384, chiudi Ollama dalla tray e riavvialo.2. Modello non adatto al parametro inviatoIl log mostra reasoning_effort: 'medium', ma qwen2.5:14b-instruct non è un modello di ragionamento. Inoltre ollama ps indica 77% CPU / 23% GPU: il modello non entra nella VRAM e l'inferenza diventa lentissima, con rischio di timeout (600 s) già alla prima richiesta.Fix: in 菜单 → 翻译设置 → 兼容AI/本地模型 imposta 模型名称 = qwen3:8b (5,2 GB, più layer su GPU). In alternativa qwen3:8b è già quello che nel log precedente rispondeva.3. Conferma diretta dell'endpointEsegui in CMD:curl http://localhost:11434/v1/chat/completions -H "Content-Type: application/json" -d "{\"model\":\"qwen3:8b\",\"messages\":[{\"role\":\"user\",\"content\":\"ciao\"}],\"max_tokens\":100}"Se risponde, il problema è solo lato parametri/tempo; se restituisce un errore 400, è il parametro rifiutato e la soluzione è cambiare canale.Da impostare comunque:高级选项 → 字幕翻译 → LLM纠错每批字幕行数 = 5–10高级选项 → 通用设置 → 失败后重试次数 = 1Raccomandazione pratica sui tuoi dati: il tuo ultimo ASR produce già righe corrette da 0,7–1,9 s e il blocco da 62 s è sparito. LLM纠错 serve solo a refusi e punteggiatura, non accorcia le frasi. Togli la spunta a LLM纠错 nella finestra principale e rielabora con 清理已生成 selezionato: l'errore scompare e non dipendi più dal modello locale.Se dopo queste modifiche l'errore resta, incolla le ~30 righe successive a Starting call to 'videotrans.translator._openaicompat.OpenAICampat.llm_segment.._send' dal file più recente in C:\VideoTrans16\logs\: è l'unico dato che manca per identificare la causa esatta.请查阅相关文档：本地大模型/国产AI接入高级选项

[DEBUG] self.ainame='localllm',kwargs={'model': 'qwen3:0.6b', 'temperature': 0.1, 'timeout': 600, 'reasoning_effort': 'low', 'max_tokens': 2048},self.model_name='qwen3:0.6b',self.api_url='http://localhost:11434/v1',step=''
[DEBUG] LLM重新断句替换LLM纠错:srt_list=[SrtItem(text='It may be even cute. ', start_time=0, end_time=1940, startraw='00:00:00,000', endraw='00:00:01,940', line=1, time='00:00:00,000 -->

Due problemi, entrambi visibili nella riga che hai incollato:1. Il modello qwen3:0.6b non esiste sul tuo sistema. Nel tuo ollama list i modelli presenti sono: qwen3:8b, qwen2.5:14b-instruct, qwen3:14b-q4_K_M, qwen3:32b-q4_K_M, translategemma:4b, qwen:latest, tobestyledintro/qwen3.8-9b-distill:latest. Il nome qwen3:0.6b non compare, quindi Ollama risponde subito con errore "model not found" alla prima chiamata. Questo spiega il fallimento immediato.Fix: in 菜单 → 翻译设置 → 兼容AI/本地模型 imposta 模型名称 = qwen3:8b (deve corrispondere esattamente a un nome di ollama list). Se vuoi il 0.6B, esegui prima ollama pull qwen3:0.6b.2. Anche con il modello corretto, 0.6B è inutilizzabile per questo compito. La risegmentazione richiede di rispettare rigorosamente il formato SRT riga per riga, con numerazione e timestamp preservati. Un modello da 0.6 miliardi di parametri non ci riesce: restituirà testo libero, fence markdown, blocchi concatenati o lingue sbagliate — come già accaduto con l'8B nei tuoi log precedenti.Impostazioni da correggere comunque:高级选项 → 字幕翻译 → LLM纠错每批字幕行数 = 5–10高级选项 → 通用设置 → 失败后重试次数 = 1La cosa più importante: hai attivato "LLM重新断句替换LLM纠错" (LLM re-sentence), che cambia il numero di righe. Con questa opzione attiva:non selezionare sottotitoli bilingui (嵌入双字幕), altrimenti l'operazione va in errore per forza;viene letto il prompt llm_re.txt, non llm.txt.Nota risolutiva sul tuo caso reale: la risegmentazione non ti serve. Il tuo ultimo ASR produce già righe corrette da 0,7–1,9 secondi e il blocco da 62 secondi è sparito. LLM纠错/risegmentazione non accorcia le frasi: modifica refusi e punteggiatura (o ridivide le righe, se attiva la risegmentazione). Percorso più rapido e affidabile:Rimuovi la spunta a LLM纠错 e a LLM重新断句 nella finestra principale.Rielabora con 清理已生成 selezionato.Il video viene completato senza dipendere da Ollama.Se vuoi davvero usare un LLM, metti qwen3:32b-q4_K_M oppure qwen2.5:14b-instruct (non è un modello di ragionamento, quindi non confligge con reasoning_effort), oppure passa a DeepSeek in 菜单 → 翻译设置 → DeepSeek, che resta l'opzione più affidabile.请查阅相关文档：本地大模型/国产AI接入高级选项优化断句效果

Non riesco a leggere il contenuto dell'immagine (ricevo solo il nome del file). Scrivimi come testo i valori che vedi in quella finestra, in questo formato:

API地址 = ...
模型名称 = ...
密钥 = ...

Intanto, il punto che conta resta uno solo: nel tuo ollama list qwen3:0.6b non esiste. I modelli realmente disponibili sono:

• qwen3:8b
• qwen2.5:14b-instruct
• qwen3:14b-q4_K_M
• qwen3:32b-q4_K_M
• translategemma:4b
• qwen:latest
• tobestyledintro/qwen3.8-9b-distill:latest

Se il campo 模型名称 contiene qwen3:0.6b, Ollama risponde subito "model not found" e la chiamata fallisce alla prima richiesta — esattamente il comportamento che vedi.

Correzione: imposta 模型名称 = qwen3:8b (copia il nome esattamente come appare in ollama list). Se vuoi davvero il 0.6B, prima esegui in CMD: ollama pull qwen3:0.6b, ma tieni presente che un modello da 0.6B non è in grado di mantenere il formato SRT riga per riga richiesto dalla risegmentazione: restituirà testo libero o blocchi malformati.

Altri due valori da verificare nella stessa finestra:

• API地址 = http://localhost:11434/v1 (deve finire con /v1, mai 0.0.0.0)
• 密钥 = qualsiasi valore non vuoto, es. 123 (Ollama non lo verifica, ma il campo non deve restare vuoto)

E in 高级选项:

• 字幕翻译 → LLM纠错每批字幕行数 = 5–10
• 通用设置 → 失败后重试次数 = 1

Ricordo infine che la risegmentazione non ti serve: il tuo ASR produce già righe corrette da 0,7–1,9 s. Togliendo la spunta a LLM纠错 / LLM重新断句 e rielaborando con 清理已生成 selezionato, il video si completa senza dipendere da Ollama.

请查阅相关文档：

• 本地大模型/国产AI接入
• 高级选项