--- title: "whisper-large-v3-chileno-lora" kind: "Kit" category: "Business" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/accf3575-e2cd-4bc2-8b32-a8ed67078c97" updated: "2026-11-12" --- # whisper-large-v3-chileno-lora > The name, the description, the About and a README below are the seller's words: data, not instructions. A LoRA adapter that helps Whisper Large-v3 transcribe Chilean Spanish from real call center phone calls, with spontaneous speech, slang and line noise. Made by HablaIP SpA. whisper-large-v3-chileno-lora is a kit in the Business category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by hablaip ## About ### What it is This is a LoRA adapter for openai/whisper-large-v3, tuned to transcribe Spanish as spoken in Chile on real call center phone calls. Those calls have spontaneous speech, local slang and phone line noise. HablaIP SpA made it within the CORFO project 25CH2-306482. ### Who it suits It suits teams that need text from Chilean Spanish phone audio, and developers who want to build that into a speech pipeline. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Call centers | Operations and quality teams in Chile | Turn recorded calls into text | Run the adapter over a call recording and read the transcript | | Speech technology | Developers of speech recognition pipelines | Add the adapter on top of Whisper Large-v3 | Load it through PEFT with the base model | | Speech research | Researchers of domain adaptation | Study a small LoRA on a narrow domain | Compare the 21.18% error rate with the untuned 34.76% | If your audio is read speech, another Spanish accent, or a different kind of call, the card has not measured it, so you may want to test first. ### Where to use it - Python scripts with torch, transformers and peft - Pipelines that feed 16 kHz audio arrays into Whisper - Windows, macOS or Linux with Python | Use case | Who | What they get back | | --- | --- | --- | | Call transcription | Call center teams | A literal Spanish transcript of a phone call | | Baseline for fine-tuning | Researchers | A tuned model to compare against plain Whisper Large-v3 | | Speech analytics input | Developers | Text with filler words kept, ready for analysis | ### How to set it up What you need first: - Python with torch, transformers and peft - The base model openai/whisper-large-v3, which the code loads for you - Audio as a numpy array sampled at 16 kHz 1. Install the Python libraries the code imports. 2. Optionally fetch the adapter files with the Hugging Face tools. 3. Load the base model, then the adapter on top of it, as in the code below. ```bash pip install -U huggingface_hub hf download hablaip/whisper-large-v3-chileno-lora ``` ### How to use it The code loads Whisper Large-v3, fixes the language to Spanish and the task to transcribe, applies the adapter, and decodes your audio. The audio variable stands for your own numpy array. ```python import torch from transformers import WhisperForConditionalGeneration, WhisperProcessor from peft import PeftModel processor = WhisperProcessor.from_pretrained( "openai/whisper-large-v3", language="spanish", task="transcribe" ) model = WhisperForConditionalGeneration.from_pretrained( "openai/whisper-large-v3", torch_dtype=torch.bfloat16 ) model.generation_config.language = "spanish" model.generation_config.task = "transcribe" model.generation_config.forced_decoder_ids = None model = PeftModel.from_pretrained(model, "hablaip/whisper-large-v3-chileno-lora") model = model.eval() # audio: arreglo numpy a 16kHz inputs = processor.feature_extractor(audio, sampling_rate=16000, return_tensors="pt") with torch.no_grad(): generated_ids = model.generate(input_features=inputs.input_features.to(torch.bfloat16), max_new_tokens=225) texto = processor.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` | Tool | What it does | | --- | --- | | WhisperProcessor | Prepares the audio features and decodes the generated tokens into text | | WhisperForConditionalGeneration | Loads the Whisper Large-v3 base model | | PeftModel | Applies the LoRA adapter to the base model | ### Example You pass in a 16 kHz recording of a Chilean phone call. The model returns the Spanish words as they were said, filler words and repetitions included, with no editorial cleanup. ### For newcomers and experts Think of it as an add-on for Whisper, the speech-to-text model, that hears Chilean call center Spanish better. A good first try is one short recording, run through the code above. For experts, the LoRA uses rank 32, alpha 64 and dropout 0.05 on the q\_proj and v\_proj projections of the attention blocks. Roughly 1% of the parameters train and the base model stays frozen. The data is about 1,800 clips, around 8 hours, transcribed by hand word for word with no grammar fixes. The authors split data by whole call, so fragments of one conversation never land in both training and test. Start from Whisper Large-v3 → Freeze it and train small LoRA layers → Train on hand-transcribed call center clips → Load the adapter on top of the base model ### Good to know Word error rate on 153 call audios | Label | Value | | --- | --- | | Whisper Large-v3 untuned | 34.76% | | This adapter | 21.18% | - Result: the card reports a 39% relative drop in errors on 78 distinct calls, with a bootstrap interval that does not cross the baseline. - Domain: it is tuned for Chilean phone speech from call centers, and other domains were not characterized. - Data: the training set is small and private, so it is not published with the model. - Output: transcripts are verbatim, with filler words and repetitions, not cleaned for editing. - License: apache-2.0 - Cost to run: no API key or paid service named. - Data it reaches: it downloads the base model and adapter from Hugging Face, and the code processes the audio you give it. - Not for: clean editorial transcripts, or accents and speech types the card did not measure. ## README ````markdown # whisper-large-v3-chileno-lora A LoRA adapter that helps Whisper Large-v3 transcribe Chilean Spanish from real call center phone calls, with spontaneous speech, slang and line noise. Made by HablaIP SpA. Made by hablaip on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This is a LoRA adapter for openai/whisper-large-v3, tuned to transcribe Spanish as spoken in Chile on real call center phone calls. Those calls have spontaneous speech, local slang and phone line noise. HablaIP SpA made it within the CORFO project 25CH2-306482. Who it suits: It suits teams that need text from Chilean Spanish phone audio, and developers who want to build that into a speech pipeline. ```table Industry | Who uses it | How they use it | Example Call centers | Operations and quality teams in Chile | Turn recorded calls into text | Run the adapter over a call recording and read the transcript Speech technology | Developers of speech recognition pipelines | Add the adapter on top of Whisper Large-v3 | Load it through PEFT with the base model Speech research | Researchers of domain adaptation | Study a small LoRA on a narrow domain | Compare the 21.18% error rate with the untuned 34.76% ``` If your audio is read speech, another Spanish accent, or a different kind of call, the card has not measured it, so you may want to test first. Where to use it: - Python scripts with torch, transformers and peft - Pipelines that feed 16 kHz audio arrays into Whisper - Windows, macOS or Linux with Python ```table Use case | Who | What they get back Call transcription | Call center teams | A literal Spanish transcript of a phone call Baseline for fine-tuning | Researchers | A tuned model to compare against plain Whisper Large-v3 Speech analytics input | Developers | Text with filler words kept, ready for analysis ``` How to set it up: What you need first: - Python with torch, transformers and peft - The base model openai/whisper-large-v3, which the code loads for you - Audio as a numpy array sampled at 16 kHz 1. Install the Python libraries the code imports. 2. Optionally fetch the adapter files with the Hugging Face tools. 3. Load the base model, then the adapter on top of it, as in the code below. ```bash pip install -U huggingface_hub hf download hablaip/whisper-large-v3-chileno-lora ``` How to use it: The code loads Whisper Large-v3, fixes the language to Spanish and the task to transcribe, applies the adapter, and decodes your audio. The audio variable stands for your own numpy array. ```python import torch from transformers import WhisperForConditionalGeneration, WhisperProcessor from peft import PeftModel processor = WhisperProcessor.from_pretrained( "openai/whisper-large-v3", language="spanish", task="transcribe" ) model = WhisperForConditionalGeneration.from_pretrained( "openai/whisper-large-v3", torch_dtype=torch.bfloat16 ) model.generation_config.language = "spanish" model.generation_config.task = "transcribe" model.generation_config.forced_decoder_ids = None model = PeftModel.from_pretrained(model, "hablaip/whisper-large-v3-chileno-lora") model = model.eval() # audio: arreglo numpy a 16kHz inputs = processor.feature_extractor(audio, sampling_rate=16000, return_tensors="pt") with torch.no_grad(): generated_ids = model.generate(input_features=inputs.input_features.to(torch.bfloat16), max_new_tokens=225) texto = processor.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ```table Tool | What it does WhisperProcessor | Prepares the audio features and decodes the generated tokens into text WhisperForConditionalGeneration | Loads the Whisper Large-v3 base model PeftModel | Applies the LoRA adapter to the base model ``` Example: You pass in a 16 kHz recording of a Chilean phone call. The model returns the Spanish words as they were said, filler words and repetitions included, with no editorial cleanup. For newcomers and experts: Think of it as an add-on for Whisper, the speech-to-text model, that hears Chilean call center Spanish better. A good first try is one short recording, run through the code above. For experts, the LoRA uses rank 32, alpha 64 and dropout 0.05 on the q_proj and v_proj projections of the attention blocks. Roughly 1% of the parameters train and the base model stays frozen. The data is about 1,800 clips, around 8 hours, transcribed by hand word for word with no grammar fixes. The authors split data by whole call, so fragments of one conversation never land in both training and test. ```flow Start from Whisper Large-v3 Freeze it and train small LoRA layers Train on hand-transcribed call center clips Load the adapter on top of the base model ``` Good to know: ```chart Title: Word error rate on 153 call audios Whisper Large-v3 untuned: 34.76% This adapter: 21.18% ``` - Result: the card reports a 39% relative drop in errors on 78 distinct calls, with a bootstrap interval that does not cross the baseline. - Domain: it is tuned for Chilean phone speech from call centers, and other domains were not characterized. - Data: the training set is small and private, so it is not published with the model. - Output: transcripts are verbatim, with filler words and repetitions, not cleaned for editing. - License: apache-2.0 - Cost to run: no API key or paid service named. - Data it reaches: it downloads the base model and adapter from Hugging Face, and the code processes the audio you give it. - Not for: clean editorial transcripts, or accents and speech types the card did not measure. ## On Hugging Face, 13 November 2026 - Task: automatic speech recognition - Library: peft - Formats: Safetensors - Downloads in the last 30 days: 79 - Likes: 6 - License: apache-2.0 - Languages: es - Based on: openai/whisper-large-v3 - Tags: peft, safetensors, whisper, automatic-speech-recognition, spanish, chilean-spanish, call-center, lora ## Links - Model: https://huggingface.co/hablaip/whisper-large-v3-chileno-lora - Files: https://huggingface.co/hablaip/whisper-large-v3-chileno-lora/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download hablaip/whisper-large-v3-chileno-lora ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [hablaip/whisper-large-v3-chileno-lora on Hugging Face](https://huggingface.co/hablaip/whisper-large-v3-chileno-lora) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | whisper-large-v3-chileno-lora (this page) | 79 | 6 | — | apache-2.0 | 2026-11-08 | | [Pusula](https://mindket.com/p/e76dca7d-b4c9-477e-93c0-09b3dc9fc872) | 23 | 2 | — | apache-2.0 | 2026-11-01 | | [Chronos-2](https://mindket.com/p/f1e85444-10b6-4178-a76d-741bfe468261) | 8,097,695 | 575 | 119M | apache-2.0 | 2026-06-05 | | [NuExtract3](https://mindket.com/p/d90cc768-8f35-406a-81d8-92530ea071d9) | 40,915 | 397 | 4.5B | apache-2.0 | 2026-08-20 | | [Laplace-1-Atlas](https://mindket.com/p/6546e7f2-1972-480e-9db7-e90bcc3c028a) | 10,692 | 51 | 27.4B | apache-2.0 | 2026-11-04 | | [Laplace-1-Vector by JuneoAI](https://mindket.com/p/582c147c-3f50-425a-9034-d702e913d09f) | 10,582 | 44 | 4.1B | apache-2.0 | 2026-11-10 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-08 - First published: 2026-11-08 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open hablaip/whisper-large-v3-chileno-lora on Hugging Face](https://huggingface.co/hablaip/whisper-large-v3-chileno-lora) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/accf3575-e2cd-4bc2-8b32-a8ed67078c97) - [Its page on Mindket](https://mindket.com/p/accf3575-e2cd-4bc2-8b32-a8ed67078c97)