| {"id": "20261112T161250Z_ce058f", "sentence_id": "p_02f106c45a", "text": "And recording a quick note here. And we're trying to do voice isolation. There is a man called Mike who's sitting on our couch. And this is a small, a very small dataset that it's very. It's an experimentary one for people speaking in the background. So there's all kinds of people. So I'm just going to describe who I am. My name is Daniel, I'm the speaker here recording on the OnePlus Nord 3 5G and other people in the room. There's a man called Mike who's looking very unwell, lying on the couch. An Australian man. Ezra is my son and he's just triggered the Roborock speaker. Hannah is my wife. And also made. It made a noise there. And we also had things. So this is a good first. First clip where we have. You can hear Ezra crying there. So this sample we have a challenge for background noise and for speaker isolation. We have non-diarized audio. So we're getting a few angles for this one here. And the purpose of this dataset, this is a very experimentary part of the project because one of the tasks that I wanted to train on for my ASR fine-tune was specifically speaker isolation. And as I've mentioned in many of these blog posts, the challenges that I encounter dictating all the time are basically in the real world. It's not a pristine studio. People are having conversations. There's like PA messages. That's what you have when you're dictating. And I thought firstly, naively that there could be just record clips, ground truth is your speech. And therefore the model will eventually learn to discard everything ranging from bar music to what have you. And as, as I've been checking out the latest models, I've realized that's not. That's. I. I don't believe that's the right approach, even if it were feasible. I came across a few days some really, really interesting models in the audio-to-audio task on Hugging Face. And one of them is specifically speaker isolation. And I did a double take when I saw this that someone's actually created a model not just for background noise removal, but for what you could call different speakers. So again, you're recording a business email to take an example, and you have friends over or you've got. Your wife is working from home. And the mechanism is something like listening to determining who the speaker is, getting a voice embedding from the first few seconds and then everyone that we recognize is a different voice we're just going to discard. So it's kind of acting at the VAD layer, so I'm going to just. It's going to take me at this point, there's so many clips, I think it's impossible to go back through it. I've definitely got clips of this background speech, but I'll try to just gather some intentionally because it's not so hard to do. And they can go into this one. So that's what this small subset is of the. Of the project.", "duration_s": 183.456, "sample_rate": 48000, "bit_depth": 16, "audio_format": "flac", "categories": ["background-speech", "speaker-isolation"], "target_terms": [], "prompt_id": "", "prompt_source": "drive-voice-note", "mode": "free-speech", "speaker": "Daniel Rosehill", "speaker_id": "8992", "speaker_gender": "male", "contributor": "Daniel Rosehill", "recorded_at": "2026-11-12T16:12:50Z", "device": "OnePlus Nord 3 5G", "microphone": "phone built-in", "environment": "home living room, others present", "transcript_status": "pending", "pronunciation": [], "task": "background-conversations", "noise_flags": ["background-speech", "baby-crying", "device-voice-prompt"], "model_hypothesis": "And recording a quick note here. And we're trying to do voice isolation. There is a man called Mike who's sitting on our couch. And this is a small, a very small data set that it's very. It's an experimentary one for people speaking in the background. So there's all kinds of people. So I'm just going to describe who I am. My name is Daniel, I'm the speaker here recording on the OnePlus Nord 3.5G and other people in the room. There's a man called Mike who's looking very unwell, lying on the couch. An Australian man. Ezra is my son and he's just triggered the roborock speaker. Hannah is my wife. And also made. It made a noise there. And we also had things. So this is a good first. First clip where we have. You can hear Ezra crying there. So this sample we have a challenge for background noise and for speaker isolation. We have non diarized audio. So we're getting a few angles for this one here. And the purpose of this data set, this is a very experimentary part of the project because one of the tasks that I wanted to train on for my ASR fine tune was specifically speaker isolation. And as I've mentioned in many of these blog posts, the challenges that I encounter dictating all the time are basically in the real world. It's not a pristine studio. People are having conversations. There's like PA messages. That's what you have when you're dictating. And I thought firstly, naively that there could be just record clips, ground truth is your speech. And therefore the model will eventually learn to discard everything ranging from bar music to what have you. And as, as I've been checking out the latest models, I've realized that's not. That's. I. I don't believe that's the right approach, even if it were feasible. I came across a few days some really, really interesting models in the audio to audio task on hugging face. And one of them is specifically speaker isolation. And I did a double take when I saw this that someone's actually created a model not just for background noise removal, but for what you could call different speakers. So again, you're recording a business email to take an example, and you have friends over or you've got. Your wife is working from home. And the mechanism is something like listening to determining who the speaker is, getting a voice embedding from the first few seconds and then everyone that we recognize is a different voice we're just going to discard. So it's kind of acting at the VAD layer, so I'm going to just. It's going to take me at this point, there's so many clips, I think it's impossible to go back through it. I've definitely got clips of this background speech, but I'll try to just gather some intentionally because it's not so hard to do. And they can go into this one. So that's what this small subset is of the. Of the project.", "asr_model": "assemblyai-universal (speaker_labels on; heard one speaker)", "notes": null, "extra": "{\"background_sources\": [{\"who\": \"Mike\", \"kind\": \"adult male speech\", \"note\": \"lying on the couch\"}, {\"who\": \"Ezra\", \"kind\": \"baby crying\"}, {\"who\": \"Hannah\", \"kind\": \"adult female speech\"}, {\"who\": \"Roborock\", \"kind\": \"device voice prompt\", \"note\": \"robot vacuum speaker, triggered by Ezra\"}], \"diarized\": false, \"source_file\": \"2026_11_12_18_12_50.opus\", \"transcript_note\": \"Foreground speech only (Daniel), verbatim. Draft = AssemblyAI with casing and names fixed by Claude (OnePlus Nord 3 5G, Roborock, Hugging Face, dataset); not yet checked against the audio by Daniel. Set transcript_status to reviewed once he has.\"}"} |
