언어부터 이미지, 오픈 웨이트부터 API까지. 기능과 실제 비용을 비교하세요.
다음 아이디어에 맞는 AI를 찾아보세요.
Fine-tuned on Multilingual Pretrained Model CLSRIL-23. The original fairseq checkpoint is present here. When using this model, make sure that your speech input is sampled at 16kHz. **Note: The result from this model is without a language model so you may witness a higher WER in some cases.**
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Check our github repo https://github.com/HeartMuLa/heartlib for a quickstart and local deployment of HeartMuLa.
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Download one of the quantizations of the text-text part, then download the mmproj audio processing model (in full BF16 precision). Ensure you are using the latest version of llama.cpp (or at least a release after the PR tag: b7593). 🚨 **Note:** This is a *preview* model. We will release a better base and a thinking version of the model in the next few weeks.
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기본 정보 등록 · 상세 정보 보완 예정
This model is a fine-tuned version of ylacombe/w2v-bert-2.0 on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - EN dataset. It achieves the following results on the evaluation set: - Loss: 0.2427 - Wer: 0.1455 - Cer: 0.0580 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - distributed_type: multi-GPU - num_devices: 3 - total_train_batch_size: 36 - total_eval_batch_size: 36 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 3.0 - mixed_precision_training: Native AMP
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기본 정보 등록 · 상세 정보 보완 예정
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기본 정보 등록 · 상세 정보 보완 예정
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기본 정보 등록 · 상세 정보 보완 예정
**Model Summary:** Granite-4.0-1b-speech is a compact and efficient speech-language model, specifically designed for multilingual automatic speech recognition (ASR) and bidirectional automatic speech translation (AST). The model was trained on a collection of public corpora comprising of diverse datasets for ASR and AST as well as synthetic datasets tailored to support Japanese ASR, keyword-biased ASR and speech translation. Granite-4.0-1b-speech was trained by modality aligning granite-4.0-1b-base to speech on publicly available open source corpora containing audio inputs and text targets. Compared to granite-speech-3.3-2b and granite-speech-3.3-8b, this model has the following additional capabilities and improvements: * Supports multilingual speech inputs in English, French, German, Spanish, Portuguese and Japanese, * Provides higher transcription accuracy for English ASR and faster inference through better encoder training and speculative decoding, * Has half the number of parameters of granite-speech-3.3-2b for running on resource-constrained devices, * Adds keyword list biasing capability for enhanced name and acronym recognition We evaluated granite-4.0-1b-speech alongside other speech-language models in the less than 8b parameter range as well as dedicated ASR and AST systems on standard benchmarks. The evaluation spanned multiple public benchmarks, with particular emphasis on English ASR tasks while also including multilingual ASR and AST for X-En and En-X translations.
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**Model Summary:** Granite-speech-3.2-8b is a compact and efficient speech-language model, specifically designed for automatic speech recognition (ASR) and automatic speech translation (AST). Granite-speech-3.2-8b uses a two-pass design, unlike integrated models that combine speech and language into a single pass. Initial calls to granite-speech-3.2-8b will transcribe audio files into text. To process the transcribed text using the underlying Granite language model, users must make a second call as each step must be explicitly initiated. The model was trained on a collection of public corpora comprising diverse datasets for ASR and AST as well as synthetic datasets tailored to support the speech translation task. Granite-speech-3.2 was trained by modality aligning granite-3.2-8b-instruct (https://huggingface.co/ibm-granite/granite-3.2-8b-instruct) to speech on publicly available open source corpora containing audio inputs and text targets. We evaluated granite-speech-3.2-8b alongside other speech-language models (SLMs) in the less than 8b parameter range as well as dedicated ASR and AST systems on standard benchmarks. The evaluation spanned multiple public benchmarks, with particular emphasis on English ASR tasks while also including AST for En-X translation.
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**Model Summary:** Granite-speech-3.3-2b is a compact and efficient speech-language model, specifically designed for automatic speech recognition (ASR) and automatic speech translation (AST). Granite-speech-3.3-2b uses a two-pass design, unlike integrated models that combine speech and language into a single pass. Initial calls to granite-speech-3.3-2b will transcribe audio files into text. To process the transcribed text using the underlying Granite language model, users must make a second call as each step must be explicitly initiated. The model was trained on a collection of public corpora comprising diverse datasets for ASR and AST as well as synthetic datasets tailored to support the speech translation task. Granite-speech-3.3-2b was trained by modality aligning granite-3.3-2b-instruct (https://huggingface.co/ibm-granite/granite-3.3-2b-instruct) to speech on publicly available open source corpora containing audio inputs and text targets. Compared to the initial release, revision 3.3.2 * supports multilingual speech inputs in English, French, German, Spanish and Portuguese, * provides transcription accuracy improvements for English ASR by using a deeper acoustic encoder and additional training data.
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**Model Summary:** Granite-speech-3.3-8b is a compact and efficient speech-language model, specifically designed for automatic speech recognition (ASR) and automatic speech translation (AST). Granite-speech-3.3-8b uses a two-pass design, unlike integrated models that combine speech and language into a single pass. Initial calls to granite-speech-3.3-8b will transcribe audio files into text. To process the transcribed text using the underlying Granite language model, users must make a second call as each step must be explicitly initiated. The model was trained on a collection of public corpora comprising diverse datasets for ASR and AST as well as synthetic datasets tailored to support the speech translation task. Granite-speech-3.3-8b was trained by modality aligning granite-3.3-8b-instruct (https://huggingface.co/ibm-granite/granite-3.3-8b-instruct) to speech on publicly available open source corpora containing audio inputs and text targets. We evaluated granite-speech-3.3-8b revision 3.3.2 alongside other speech-language models in the less than 8b parameter range as well as dedicated ASR and AST systems on standard benchmarks. The evaluation spanned multiple public benchmarks, with particular emphasis on English ASR tasks while also including multilingual ASR and AST for X-En and En-X translations.
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**Model Summary:** Granite Speech 4.1 2B is a compact and efficient speech-language model, specifically designed for multilingual automatic speech recognition (ASR) and bidirectional automatic speech translation (AST) for English, French, German, Spanish, Portuguese and Japanese. The model was trained on 174,000 hours of audio from public corpora for ASR and AST as well as synthetic datasets tailored to support Japanese ASR, keyword-biased ASR and speech translation. Granite Speech 4.1 2B was trained by modality aligning an intermediate checkpoint of granite-4.0-1b-base to speech on publicly available open source corpora containing audio inputs and text targets. Compared to its predecessor granite-4.0-1b-speech, this model has the same parameter count (the new naming convention reflects actual instead of base LLM size) and provides additional capabilities and improvements: * Higher transcription accuracy for multilingual ASR due to a novel dual-head CTC encoder with both graphemic and BPE outputs and frame importance sampling to focus on informative parts of the audio * Punctuation and truecasing for ASR and AST in all languages (including German noun capitalization) with a simple prompt change * Better keyword list biasing capability for enhanced recognition of names, acronyms and technical jargon Two additional model variants explore different capabilities and inference optimization: * granite-speech-4.1-2b-plus adds speaker-attributed ASR and word-level time stamps * granite-speech-4.1-2b-nar introduces a novel non-autoregressive architecture for higher throughput
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We have updated the model weights and configuration. The updated model is more accurate, especially for keyword biasing and for word-level timestamps on long samples. It is a drop-in replacement for the previous version, but we have also updated our usage recommendations to get the best performance. In the tables below, GSP v2610 refers to the updated model and GSP v2604 to the original release. Granite-Speech-4.1-2B-Plus (GSP) has similar capabilities to the Granite-Speech-4.1-2B model. The plus model adds two new community-requested rich transcription features that can be activated with a simple prompt change: speaker-attributed ASR (speaker labels and word transcripts) and word-level timing information. Unlike the base model, the plus model doesn't provide punctuation and capitalization.
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> 🎉 **NEW (October 21, 2026): Granite Speech 5.0 TurboCTC is now multilingual!** > 🌍 **7 Languages** (En, Fr, De, Es, Pt, It, Ja) > ❓ **Native punctuation & capitalization** > 🚀 **Better English WER (5.51% vs. 5.78%)** > ⏪ **Previous English-only release: revision 947f59a** **Model Summary:** Granite Speech 5.0 TurboCTC is a compact 470 million parameter multilingual ASR model for English, French, German, Spanish, Portuguese, Italian and Japanese with very high inference speed that is well suited for deployment on laptops, smartphones and other edge devices. The model consists of a conformer acoustic encoder with block self-attention, self-conditioning and temporal downsampling with an output layer corresponding to 16,384 multilingual BPE units. It was trained on approximately 153,000 hours of multilingual audio from public corpora using Connectionist Temporal Classification (CTC) and inference is done non-autoregressively with greedy decoding. The model outputs punctuated and capitalized transcripts. We evaluated granite-speech-5.0-470m-turboctc on standard short-form English ASR benchmarks from the Open ASR leaderboard. Despite adding six languages and native punctuation, the multilingual update improves the average English WER over the original English-only release (5.51% vs. 5.78%):
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> **Disclaimer:** This model is intended for research and non-commercial use-only. See Granite Speech 5.0 Turbo CTC if interested in commercial use. **Model Summary:** Granite Speech 5.0 TurboCTC Non-commercial is a compact 470 million parameter English ASR model with very high inference speed that is released for research and noncommercial purposes only. We invite users to refer to granite-speech-5.0-470m-turboctc for other use cases. The model consists of a conformer acoustic encoder with block self-attention, self-conditioning and temporal downsampling with an output layer corresponding to 16,384 BPE units. It was trained on approximately 75,000 hours of English audio from public corpora using Connectionist Temporal Classification (CTC) and inference is done non-autoregressively with greedy decoding.
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This model is a fine-tuned version of omarxadel/wav2vec2-large-xlsr-53-arabic-egyptian, enhancing **Egyptian Arabic**, **Modern Standard Arabic (MSA)** and **Gulf / Levantine Arabic** for Automatic Speech Recognition. It was trained on a diverse combination of publicly available and custom-collected Arabic speech datasets, including: \*: *Whisper models were decoded using beam search (`beam_size = 5`) and evaluated using `BasicTextNormalizer` with `remove_diacritics=False` and `split_letters=False`, applied to both predictions and reference text.*
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