Context on what we are doing

We need to create a pipeline to generate transcripts from gemini-3-flash model from google. Now we would need a proper prompt which i've crafted here a prompt @prompt.txt which is a high level overview of what and how i want the model to act or behave when generating transcripts. and then i'm gonna give you my whole context on how to and where to and when to do everything, so we can plan accordingly from the start. 


about sourcing the data and how they are pre-cleaned

firstly to start with, my data is in-the-wild youtube data. so i've downloaded youtube videos and uploaded them to R2 and each of the video in R2 is now in videoID.webm. Now my Goal is to use these videos as source of audio and  create some insanely high quality ASR and TTS models.  Now i've done a basic preprocessing steps inorder to convert these youtube videos into a single speaker segments based on VAD activity and speaker diarization. Now the issue here is this process is not performed at 100% accuracy. meaning, since the videos are youtube podcasts, there will always be many acknowledgements like, haa, yaa, ohh, stuff like that. and these segmentations or speaker similarity models are quite not that precise to catch those things and some quite slipped through. 


R2 format and addressing real issue and accepting minor error. 


Now the data that i have in R2 is in the format of videoID.tar and upon unzipping it we get metadata.json and a segments/ folder with all segments and speaker numbered based on timestamps.flac format and now these segments are not 100% completely single speakered but mostly are. can assume few errors. Given a big scale, i'm assuming all tehse are fine. and also now, one more issue here is that, these segments are not cleanly started with silence and ends in silence on how a TTS system performs. but some of teh segments are cut or ended or started abruptly in teh previous pre-cleanup phase, and might start with the middle of the sentence of a speaker. 


Now that's everything about my data. And i am now supporting 11 indian languages and english. so that's 12. - Hindi, Marathi, Telugu, Tamil, Kannada, Malayalam, Gujarati, Punjabi, Bengali, Assamese, Odia, English. 

And all of these VideoIDs have simple metadata that is stored in supabase and has language tagged and other details mentioned in it like number of segments, etc. but mostly for this transcription task we might need just the language id for the videoID cause we might need to pass it in the prompt. 

Now coming to the prompt. 

i've prepared a pretty decent prompt. although i need you to go through the prompt and understand what it does and how it is used by the LLM and especially gemini-3-flash model with proper settings and temperature. i've noticed proper results with temperature being 0 despite the gemini team mentioning it doesnt benefit from temperature being 0 and fails in reasoning and math tasks, since this is audio transcription using an LLM, i think determinism is the key here. and coming to the other fields in the prompt that i've crafted, 

i will explain teh JSOn schema so you'd know if you need to tweak the prompt as per requirements and you'd best know how these models perform properly right. assunming you might twaeak it better than i do.


{
  "type": "object",
  "properties": {
    "transcription": {
      "type": "string",
      "description": "Native script transcription with minimal punctuation" 
    },
    "tagged": {
      "type": "string",
      "description": "Code-mixed transcription with audio event tags"
    },
    "speaker": {
      "type": "object",
      "description": "Speaker metadata",
      "properties": {
        "emotion": {
          "type": "string",
          "enum": ["neutral", "happy", "sad", "angry", "excited", "surprised"]
        },
        "speaking_style": {
          "type": "string",
          "enum": ["conversational", "narrative", "excited", "calm", "emphatic", "sarcastic", "formal"]
        },
        "pace": {
          "type": "string",
          "enum": ["slow", "normal", "fast"]
        },
        "accent": {
          "type": "string",
          "description": "Regional accent/dialect or empty string"
        }
      },
      "required": ["emotion", "speaking_style", "pace"],
      "additionalProperties": false
    },
    "detected_language": {
      "type": "string",
      "description": "Language actually spoken in the audio"
    }
  },
  "required": ["transcription", "tagged", "speaker", "detected_language"],
  "additionalProperties": false
}

transcription - -> this is the response i need as a global source of truth. and i here i will need this to be properly planned. Now the language might be mixed or single or can be anything. for all we know the youtube video is tagged to be a particular language, but what if there is a particular sentence in the language is spoken in english and then we have passed the language as telugu to gemini along with the prompt. and it might get confused. but if we dont, gemini sometimes gets it wrong. what is teh best course of action here ? and hence i came up with asking the model it's confident field of detected_language field where it returns a different or same if matched language tag right ? in that case, we need to mention this behaviour in the prompt. to handle properly. 

addressing key thing about transcription, the language or the script to maintain. Now the language can be telugu or something, if there is a word spoken in english or something like that mixed, is it better if we ask gemini to return code-mixed ? or is it better if we ask it to produce single language only and transliterated script instead. i will later anyway convert all of this to a different script required later using differnt llm call again, but i need this LLM call to gemini which includes audio(since it is costly and has more input tokens, to be accurate), so do you suggest if ask single language transliterated, gemini performs accurately or does it perform better if we just asked code-mixed ? and also remember we will need to get punctuated text and numbers and symbols or tone based punctuations to be inserted. if we want, we can always remove them and train the models. but we can't add them later on in the training right. so punctuations are always on. plan for the code-mixed part or single script for consistency, either way, we can always make another LLM call to convert to another, but the present one has to be fucking 100% accurately done. 


and coming to the audio tagged part, this part is experimental for me. i want to on later experiment on audio events for making my ASR model detect audio events like, snort, laugh, and etc. and i want to control the TTS model to include audio events like laugh, cough and etc. so now that we are getting our audio processed by a superior model like gemini, it's better if we can get all these details added too. we would need to tweak the input prompt accordingly. also remember to have the same transcription field to be in tagged field too, just audio events inserted with [] brackets arounnd the event tags. to be deterministic and limited on these event tags, i wanted to include a fixed list of some 10 events. if you think these might or have different ones might be present in youtube podcast audios, it's better if we reason here as well. and ask gemini to only insert if it's confident only.


so far, we've asked gemini to create a transcription of the audio and insert audio events based on it, probably ask it to backtrack or re-listen before creating the audio-events tagged field and then there are few other details we might need like the speaker metadata as well. 

gemini can confidently tell what is the emotion or tone of the speaker. and speking style and the pace of teh audio spoken, is it slow, fast, what is it and also it can detect the accent or geo-location of the  spoken person in india if it can detect if it's confident. probably we can also ask for regional accent as required field too, but i'd leave it to you to reason and act accordingly. 


now that the prompt is done. let's talk about how we shall prepare the audio segments before we send to gemini. 

As i've said before, the audio segments might or might not be truely cut at the lowest energy or no voice activity point. if the audio is starting with silence, and has a little milliseconds of time before it's VAD starts, or energy starts, it means we are starting from a well cut segment and gemini truely gets this one properly. and if we are starting from a non-silence point or VAD energy is present from immediate starting of the segment, or amplitude is present, that means we have to find the lowest energy point or a proper sentence starting to be able to create that perfect sentence for gemini to be able to transcribe it properly without hallucinating. we need a proper strategy. even if it means we are cut down to 30-40% of the total audio, we need that fix. and ensure proper silence like some 100ms is present on both ends so it feels like a proper setnence and also 100ms is just a number. but i'd like to hear to your opinion on total understanding here. 


now that sentence polishing is done, let's talk about validating the transcripts that gemini has returned. would like your opinions on how can we handle this part, like we can also get it reviewed by a superior model like gemini-3-pro, but the point is, we need to validate and provide a proper score. I was thinking the models like MFA aligners can be useful here, but are there any aligners for indic ? and also, how quick are these aligners validate the prompt ? also, is there a programmatic way to convert the current prompt from telugu or other language to roman, and validate the correctness of the transcription. any CTC or G2G models that are trained on these romanized text, that can get this done like validate the transcript ? what do you generally suggest or do here ? we can first ideate, then get all the scores and do another run later, to pull all the low scored ones and handle it properly.