IBM AI-এর মাল্টিলিঙ্গুয়াল ট্রান্সক্রিপশন ও অনুবাদে Granite Speech 5.0 2B-এর উন্মোচন

এটি ফিডে পাওয়া অংশবিশেষ। এটি সংক্ষিপ্ত বা প্রকাশকের মূল ভাষায় হতে পারে।

IBM has released Granite Speech 5.0 2B, a new open speech-language model for transcription and translation. Granite Speech 5.0 2B is a 2B-parameter model from IBM that turns speech into text in 6 languages. It ships under Apache 2.0. IBM also updated 2 existing models: the edge-focused Turbo CTC and the diarization-focused 4.1 2B Plus. For developers, the release covers accurate transcription, fast on-device transcription and speaker-attributed transcripts. TL;DR Size: 2B parameters, dense. LLM backbone uses YaRN extension to 16k positions. IBM recommends audio segments of up to about 2 minutes. Runs on: GPU via Transformers or vLLM, Apple Silicon via mlx-audio, local CPU/Mac via llama.cpp GGUF (Q8_0 recommended). Performance: #2 open-weight model on the Open ASR Leaderboard, 5.68% average English WER. Best: 0.99% WER on LibriSpeech Clean. Worst: 13.79% WER on the leaderboard’s private conversational set. How does Granite Speech 5.0 2B work? The model has 3 parts, per the model card. A speech encoder of 16 Conformer blocks is trained with CTC. It uses block attention over 4-second audio blocks. A 2-layer window q-former then projects and downsamples the acoustic embeddings. Total downsampling is 10x, so the LLM sees embeddings at 10 Hz. The LLM is an intermediate checkpoint of granite-4.0-1b-base. IBM extended it to 16k positions with YaRN and fine-tuned it with LoRA adapters. Training used 390,000 hours of public audio plus synthetic data. The synthetic sets target Japanese ASR, keyword biasing and translation. Training took 40 days on 8 H100 GPUs. What is new versus Granite Speech 4.1 2B? Language ID prompting: name the input language in the prompt to steer transcription. Keyword lists up to 2,000 terms: bias output toward product names, people and acronyms. Punctuation and capitalization on request: capitalization F1 runs from 90.16 to 99.51 across test sets. Robustness: better handling of noise, reverberation and distant microphones. How does Gr