nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B · Hugging Face
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B/blob/main/hero.png)
**Nemotron 3.5 Transcribe is an open-weight, offline speech and audio model** for transcription in 40 languages, with support for recordings up to one hour and a 65,536-token context. Its hybrid mixture-of-experts architecture activates approximately **4.3B parameters**, including the audio encoder, and it is released in BF16, FP8, and NVFP4 precision.
Get started ↓ · Explore the tasks ↓ · See the evidence ↓ · NeMo Speech ↗
Table of contents
- Training, Testing, and Evaluation Datasets
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#model-overview) Model Overview
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#description) Description
NVIDIA Nemotron-3.5-Transcribe is an offline speech and audio model that processes recordings, together with text instructions and optional context, into text responses.
NVIDIA developed this Speech-Augmented Language Model (SALM) for multilingual transcription, speech translation, contextual transcription, speaker-attributed transcription, rich transcription, timestamp prediction, language identification, and audio question answering. The training mixture also includes a small amount of text instruction following and PII tagging/redaction tasks.
This model is ready for commercial or non-commercial use.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#model-summary) Model Summary
| | | | --- | --- | | **Total Parameters** | 34B (4.3B active), including the audio encoder | | **Architecture** | Mamba-2–Transformer hybrid Mixture of Experts language model with a Parallel Expert Encoder (Transformer ASR encoder and speaker encoder) |
| **Precision** | BF16 (full-precision reference weights); FP8 and NVFP4 variants are available | | **Context Length** | 65,536 tokens; recordings up to 1 hour | | **Inputs / Outputs** | Audio and text / text, with optional word timestamps | | **Languages** | 40; see the language support matrix | | **Tasks** | Transcription in verbatim or written form, contextual transcription, speaker-attributed transcription, rich transcription, speech translation, language identification, and audio question answering | | **Deployment** | 1× H100 80 GB; see the hardware compatibility table | | **Speculative Decoding** | Native Multi-Token Prediction (MTP); optional DFlash2 draft model | | **Reasoning Mode** | Configurable through the chat template (`enable_thinking=True/False`) | | **Recommended Sampling** | Transcription, translation, and language identification: temperature 0, reasoning off; audio understanding: temperature 0.6, top_p 0.95, reasoning on | | **Best For** | Highest accuracy, customization, and fine-tuning | | **License** | OpenMDW-1.1 |
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#results-at-a-glance) Results at a Glance
Results captured on October 12, 2026. Error rates are percentages. **↓ Lower is better.** Complete results are listed under Benchmark Results.
**Best published results**
| Capability | Benchmark | Metric | Nemotron 3.5 Transcribe | Next best published | | --- | --- | --- | --- | --- | | Multi-speaker ASR | AMI, CALLHOME, Mixer 6, NOTSOFAR-1 full sessions | Mean cpWER ↓ | **15.80** | DiCoW v3.3 + DiariZen 16.83 | | Style control | Preference-ASR | WER ↓, default / instructed | **7.56 / 6.16** | Canary-Qwen 8.16 / 8.36 |
**Best open-weight results**
| Capability | Benchmark | Metric | Nemotron 3.5 Transcribe | Next best open-weight model | | --- | --- | --- | --- | --- | | English ASR | Open ASR Leaderboard, 9 public sets | Mean WER ↓ | **4.71** | Qwen3-ASR-1.7B 4.94 | | Long-form ASR | Open ASR Leaderboard, long-form | Mean WER ↓ | **10.98** | Parakeet-TDT-0.6B-v3 13.37 | | Multilingual ASR | Multilingual Open ASR Leaderboard, 7 languages | Mean WER ↓ | **4.62** | Qwen3-ASR-1.7B 6.49 |
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#efficiency) Efficiency
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B/blob/main/serving_performance.png)
With the optimized vLLM implementation in the Nemotron Transcribe NIM, one RTX PRO 6000 Blackwell Server Edition GPU transcribes up to 2,238 seconds of audio per second (NVFP4 with DFlash2 speculative decoding) and returns the transcript of a 30-second recording in 0.13 seconds. These serving optimizations are not yet part of the open-source release; we aim to upstream them to NeMo Speech and vLLM at a later date. The vLLM integration shipped in NeMo Speech on the release day is intended for model development and is expected to be roughly two times slower.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#quick-start) Quick Start
The example below serves this checkpoint on **one H100 80 GB** with vLLM 0.32.0 and the latest NeMo Speech main branch. Install both and download the checkpoint as described in Environment Setup.
``` export VLLM_PLUGINS=nemo_speechlm export VLLM_MAX_AUDIO_DECODE_DURATION_S=3600 export VLLM_MAX_AUDIO_CLIP_FILESIZE_MB=180
Word timestamps require the V1 model runner.
export VLLM_USE_V2_MODEL_RUNNER=0 vllm serve ./NVIDIA-Nemotron-3.5-Transcribe-30B-A4B \ --served-model-name nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B \ --trust-remote-code --dtype bfloat16 --gpu-memory-utilization 0.94 \ --max-model-len 65536 --max-num-batched-tokens 4096 --max-num-seqs 192 \ --enable-chunked-prefill --no-enable-prefix-caching \ --use-replayssm --mamba-backend flashinfer --mamba-cache-mode none \ --mm-processor-cache-gb 0 --no-async-scheduling \ --limit-mm-per-prompt '{"audio":{"count":6,"length":120000000}}' \ --speculative-config '{"method":"mtp","num_speculative_tokens":8}' \ --reasoning-parser nemotron_v3 \ --trust-request-mm-kwargs \ --middleware nemo.collections.speechlm2.vllm.salm.ctc_serving.ctc_timestamp_middleware ```
Once the server has loaded the model, transcribe a recording:
``` import base64 from openai import OpenAI
client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="EMPTY") audio = base64.b64encode(open("audio.wav", "rb").read()).decode() response = client.chat.completions.create( model="nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B", messages=[{"role": "user", "content": [ {"type": "text", "text": "Transcribe the following audio."}, {"type": "audio_url", "audio_url": {"url": f"data:audio/wav;base64,{audio}"}}, ]}], temperature=0, extra_body={"chat_template_kwargs": {"enable_thinking": False}, "skip_special_tokens": False}, ) print(response.choices[0].message.content) ```
See Inference Setup and Prompts for other configurations, reasoning, word timestamps, and task-specific prompts.
To run the model in-process with Hugging Face Transformers instead, install it from source and transcribe a recording:
``` pip install git+https://github.com/huggingface/transformers ```
``` from transformers import AutoModelForMultimodalLM, AutoProcessor
model_id = "nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B" samples = "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/" processor = AutoProcessor.from_pretrained(model_id) model = AutoModelForMultimodalLM.from_pretrained(model_id, device_map="auto")
inputs = processor.apply_transcription_request(samples + "en-Alice_woman.wav").to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(processor.decode(outputs[:, inputs.input_ids.shape[1] :], skip_special_tokens=True)) ```
See Transformers usage 🤗 for more.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#prompt-guide) Prompt Guide
Choose the transcription style with the instruction in the user message; keep the system prompt empty for transcription and translation.
| What you need | What to ask for | Where it fits | | --- | --- | --- | | The words as spoken | `Audio transcription. Language: as spoken. Wording: verbatim, including fillers and repetitions. Formatting: punctuation and capitalization. Output: transcript only.` | Research transcripts and detailed review |
| Readable text | `Audio transcription. Language: as spoken. Wording: omit fillers and accidental repetitions; keep the remaining spoken wording unchanged. Formatting: punctuation and capitalization. Output: transcript only.` | Captions, notes, and searchable recordings | | Domain vocabulary | `Transcribe with punctuation and capitalization. Context terms: <names and terms>.` | Earnings calls, support recordings, and technical discussions |
| Who said what, and when | `Transcribe this audio with punctuation and capitalization. Include all spoken words and verbal fillers. Write numbers in their standard written form. Put speaker tags <spk:0> <spk:1> <spk:2> <spk:3> on the transcription.` Enable word timestamps in the request. | Meeting review and editing workflows |
| Only the person at the microphone (experimental) | `Transcribe this audio. Include all spoken words and verbal fillers. Write numbers as spoken. No punctuation or capitalization. Transcribe only the speaker nearest to the microphone. Do not transcribe any other voices.` | Dictation and voice interfaces with background speakers | | Speech in another language | `Please translate the given speech to <target language>.` | Multilingual media and localization | | An answer from the recording | Ask a specific question about the audio and enable reasoning. | Audio search, review, and understanding |
**This is an offline model.** Internal audio chunking does not make it a real-time streaming ASR service. Language and task support vary; check the language support matrix. See Prompt Recommendations for more tasks and All Evaluated Prompts for the exact benchmark prompts.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#license--terms-of-use) License / Terms of Use
Use of this model is governed by the OpenMDW License Agreement, version 1.1 (OpenMDW-1.1). Users are responsible for complying with this license and with any third-party licenses or notices applicable to components included in the distributed model materials.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#deployment-geography) Deployment Geography
Global
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#use-case) Use Case
Intended users are developers and researchers building speech interfaces, transcription and translation tools, meeting and media processing, customer-service analytics, and audio-understanding applications. Text context can supply task instructions and vocabulary hints. Speaker labels are intended to distinguish speakers within a recording; they do not establish real-world identity.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#release-date) Release Date
- Build.Nvidia.com 10/28/2026
- Github 10/28/2026 via NVIDIA NeMo Speech
- Hugging Face 10/28/2026 via NVIDIA-Nemotron-3.5-Transcribe-30B-A4B
- NGC 10/28/2026 via NVIDIA-Nemotron-3.5-Transcribe-30B-A4B
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#references) References
- Model page: NVIDIA-Nemotron-3.5-Transcribe-30B-A4B
- Technical report: Nemotron 3.5 Transcribe Technical Report
- Base language model: NVIDIA Nemotron-3.5 Lightning 30B-A3B BF16
- Quick-start and inference documentation: see Inference quick start guide.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#model-architecture) Model Architecture
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B/blob/main/architecture.png)
**Architecture Type:** Other - hybrid Mamba-Transformer mixture-of-experts (MoE) language model with a Transformer audio encoder
**Network Architecture:** Nemotron 3.5 Lightning hybrid Mamba-Transformer MoE with Transformer ASR encoder and Sortformer speaker encoder
**Design:** Audio perception module consisting of transformer ASR encoder and Nemotron-3-Diarization speaker encoder, a connectionist temporal classification (CTC) timestamp alignment head, and NVIDIA Nemotron-3.5 Lightning 30B-A3B language backbone. The perception configuration uses a 1,280-dimensional identity connector followed by a learned projection to 2,688 dimensions. Audio features are combined with the text prompt for text generation. The model enables joint multi-token prediction (MTP), with two trained next-token prediction depths using a repeated layer and retaining the existing head. Reusing the repeated layer during inference supports up to eight speculative tokens. A dedicated DFlash2 draft model, a block-diffusion drafter based on DFlash, is also provided as an option for speculative decoding.
**Base model:**`nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16`; 600M parameter pretrained ASR transformer; and approximately 99M parameter Nemotron-3-Diarization speaker encoder.
**Parameter count:** The language backbone has approximately 3.5B active parameters. Together with the approximately 800M-parameter joint audio encoder, the model has approximately 4.3B active parameters.
**Precision:** Weights are released in bfloat16 (BF16), with FP8 and NVFP4 quantized variants.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#language-support-matrix) Language Support Matrix
- ✓ Supported
- — Not specified
ASR means automatic speech recognition; code-switching means using more than one language within a recording. Translation support covers both directions between English and the listed language.
Regional variants listed in parentheses are supported as part of their language.
The matrix covers transcription and translation. Audio understanding, reasoning, and spoken instruction following were evaluated only in English and may not work reliably in other languages.
**Quality levels:**
- **Leading**: state of the art or near state of the art out of the box
- **Production-ready**: production-level quality
- **Supported**: supported, may need fine-tuning
| Language | ASR | Translation ↔ English | Multispeaker ASR | Code-switching | Quality level | | --- | --- | --- | --- | --- | --- | | Czech | ✓ | ✓ | — | ✓ | Leading | | Dutch | ✓ | ✓ | — | ✓ | Leading | | English (United States, India, United Kingdom) | ✓ | ✓ | ✓ | ✓ | Leading | | French (France, Canada) | ✓ | ✓ | ✓ | ✓ | Leading | | German | ✓ | ✓ | ✓ | ✓ | Leading | | Hindi | ✓ | ✓ | ✓ | ✓ | Leading | | Italian | ✓ | ✓ | ✓ | ✓ | Leading | | Korean | ✓ | ✓ | ✓ | ✓ | Leading | | Polish | ✓ | ✓ | — | ✓ | Leading | | Portuguese (Portugal, Brazil) | ✓ | ✓ | ✓ | ✓ | Leading | | Russian | ✓ | ✓ | ✓ | ✓ | Leading | | Slovak | ✓ | ✓ | — | ✓ | Leading | | Spanish (United States, Spain) | ✓ | ✓ | ✓ | ✓ | Leading | | Bulgarian | ✓ | ✓ | — | ✓ | Production-ready | | Croatian | ✓ | ✓ | — | ✓ | Production-ready | | Danish | ✓ | ✓ | — | ✓ | Production-ready | | Estonian | ✓ | ✓ | — | ✓ | Production-ready | | Finnish | ✓ | ✓ | — | ✓ | Production-ready | | Hungarian | ✓ | ✓ | — | ✓ | Production-ready | | Japanese | ✓ | ✓ | ✓ | ✓ | Production-ready | | Latvian | ✓ | ✓ | — | ✓ | Production-ready | | Lithuanian | ✓ | ✓ | — | ✓ | Production-ready | | Romanian | ✓ | ✓ | — | ✓ | Production-ready | | Slovenian | ✓ | ✓ | — | ✓ | Production-ready | | Swedish | ✓ | ✓ | — | ✓ | Production-ready | | Thai | ✓ | ✓ | ✓ | ✓ | Production-ready | | Turkish | ✓ | — | — | ✓ | Production-ready | | Ukrainian | ✓ | ✓ | — | ✓ | Production-ready | | Arabic | ✓ | ✓ | ✓ | ✓ | Supported | | Bengali | ✓ | — | — | ✓ | Supported | | Chinese Mandarin | ✓ | ✓ | ✓ | ✓ | Supported | | Greek | ✓ | ✓ | — | ✓ | Supported | | Hebrew | ✓ | ✓ | — | ✓ | Supported | | Indonesian | ✓ | — | — | ✓ | Supported | | Maltese | ✓ | ✓ | — | ✓ | Supported | | Marathi | ✓ | — | — | ✓ | Supported | | Norwegian | — | — | — | — | Supported | | Tagalog | ✓ | — | — | ✓ | Supported | | Tamil | ✓ | — | — | ✓ | Supported | | Telugu | ✓ | — | — | ✓ | Supported | | Vietnamese | ✓ | — | — | ✓ | Supported |
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#inputs) Inputs
| Property | Draft specification | | --- | --- | | Types | Audio and text. | | Formats | Audio: .wav, .flac, .mp3, .ogg (any format that can be decoded to PCM). Text: String (plain text or chat messages). | | Parameters | Audio: one-dimensional waveform per processed channel. Text: one-dimensional token sequence. | | Sampling and preprocessing | Recipe audio sampling rate: 16 kHz. Use the exported model's preprocessor and `nemotron3p5` prompt format. Encoder feature stride is configured as approximately 80 ms per audio token equivalent. | | Limits | The model is trained with recordings up to 1 hour long, and its maximum context size in training was 65536 tokens. It is not guaranteed to generalize beyond these limits. | | Audio chunking | The encoder uses 30-second post-feature chunks. This is an internal processing choice, not a 30-second recording limit or a streaming latency claim. | | Language coverage | See the 40-language support matrix. |
**Other Properties Related to Input:** The model processes 16kHz single-channel audio decoded to a float32 PCM waveform. The maximum supported audio length is up to 1 hour.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#outputs) Outputs
| Property | Specification | | --- | --- | | Types | Text. | | Formats | String. | | Parameters | One-dimensional (1D) token sequence. | | Content | Depending on the prompt: transcripts, translations, speaker/event tags, language labels, answers, or PII annotations/redactions. |
**Optional structured output:** With timestamp capture enabled in vLLM (an open-source LLM inference and serving engine), the response also includes a `ctc_timestamps` object containing word text, start and end times in seconds relative to the input audio, speaker tags, diarization segments, a mapping from transcript speaker tags to diarization speakers, and an alignment-error field. See Word Timestamps and Speaker Diarization.
**Other Properties Related to Output:** Punctuation, normalization, speaker attribution, and event annotations depend on the task and prompt.
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#software-integration) Software Integration
**Runtime Engine(s):** Training is done with NeMo Speech with the NeMo Automodel backend, PyTorch, and a Lhotse dataloader. AIStore was used for data storage throughout the model development. The recipe uses Transformer Engine attention/linear kernels, grouped GEMM experts, and DeepEP dispatch. Inference is done with SpeechLM-enabled vLLM with NeMo Skills orchestration.
**vLLM inference software:** vLLM **0.32.0** and the latest NeMo Speech main branch. Use the inference setup.
**SGLang-Omni inference software:**SGLang-Omni**0.2.0** (on SGLang **0.5.22**). See SGLang-Omni.
**Operating systems:** Linux and Windows. Linux runtimes can also run on Windows through WSL2. GGML supports both Linux and native Windows.
**Supported Hardware Microarchitecture Compatibility:** NVIDIA Ampere, NVIDIA Blackwell, NVIDIA Hopper, NVIDIA Lovelace
| Architecture | Hardware | Precision | Inference runtimes | Operating systems | | --- | --- | --- | --- | --- | | NVIDIA Ampere | A100 80GB SXM/NVLink | BF16 | vLLM 0.32.0, Hugging Face Transformers | Linux; Windows through WSL2 | | NVIDIA Blackwell | B200 SXM/NVLink | BF16, FP8, NVFP4 | vLLM 0.32.0, Hugging Face Transformers | Linux; Windows through WSL2 | | NVIDIA Blackwell | DGX Spark (GB10) | BF16, FP8, NVFP4 | vLLM 0.32.0, Hugging Face Transformers | Linux; Windows through WSL2 | | NVIDIA Blackwell | GB200 | BF16, FP8, NVFP4 | vLLM 0.32.0, Hugging Face Transformers | Linux; Windows through WSL2 | | NVIDIA Blackwell | GB300 DGX Station | BF16, FP8, NVFP4 | vLLM 0.32.0, Hugging Face Transformers | Linux; Windows through WSL2 | | NVIDIA Blackwell | Jetson Thor | BF16, FP8, NVFP4 | vLLM 0.32.0, Hugging Face Transformers | Linux; Windows through WSL2 | | NVIDIA Blackwell | RTX 5090 | NVFP4 | vLLM 0.32.0, Hugging Face Transformers, GGML | Linux; Windows through WSL2; native Windows with GGML | | NVIDIA Blackwell | RTX PRO 6000 Blackwell Server Edition | BF16, FP8, NVFP4 | vLLM 0.32.0, Hugging Face Transformers, GGML, SGLang-Omni 0.2.0 | Linux; Windows through WSL2; native Windows with GGML | | NVIDIA Hopper | H100 | BF16, FP8 | vLLM 0.32.0, Hugging Face Transformers, SGLang-Omni 0.2.0 | Linux; Windows through WSL2 | | NVIDIA Hopper | H200 | BF16, FP8 | vLLM 0.32.0, Hugging Face Transformers | Linux; Windows through WSL2 | | NVIDIA Lovelace | L40S PCIe 48GB | FP8 | vLLM 0.32.0, Hugging Face Transformers | Linux; Windows through WSL2 |
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#model-versions) Model Version(s)
- NVIDIA-Nemotron-3.5-Transcribe-30B-A4B (BF16)
- NVIDIA-Nemotron-3.5-Transcribe-30B-A4B-FP8 and NVIDIA-Nemotron-3.5-Transcribe-30B-A4B-NVFP4: post-training quantized variants; see the hardware compatibility table above to choose a precision supported by your GPU
- NVIDIA-Nemotron-3.5-Transcribe-30B-A4B-DFlash2: optional draft model for speculative decoding
The model can be integrated into an AI system as a service, through the OpenAI-compatible API of a vLLM or SGLang-Omni server, or in-process, with Hugging Face Transformers or, on supported GPUs, with GGML (see the Inference quick start guide). It can be adapted to new domains and tasks with the fine-tuning toolkit in NeMo Speech.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#checkpoint-contents) Checkpoint Contents
The Nemotron-3.5-Transcribe checkpoint includes the CTC heads and speaker encoder used for word timestamps and speaker diarization.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#training-testing-and-evaluation-datasets) Training, Testing, and Evaluation Datasets
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#training-dataset) Training Dataset
**Data Modality:** Audio, Text
**Audio Training Data Size:** More than 1 Million Hours (approximately 1.72 million hours across unique indexed audio examples; 6.53 million hours including repeated exposure)
**Text Training Data Size:** 1 Billion to 10 Trillion Tokens (5.6 billion unique tokens)
**Data Collection Method by dataset:** Hybrid: Manually-Collected, Automated, Synthetic
**Labeling Method by dataset:** Hybrid: Manually-Labeled, Automatic/Sensors, Synthetic
**Properties:** Approximately 372 million unique audio examples (1.72 million hours) and 5.6 billion unique text tokens. Audio is paired with text instructions and targets: transcripts, translations, speaker-attributed transcripts, captions, answers, and labels; a small share of the data is text-only. The content comprises public and third-party speech corpora (read speech, conversations, meetings, broadcasts, and earnings calls), web-derived speech, sound, and music, pseudo-labeled transcripts and translations, constructed multi-speaker mixtures, and synthetic data such as synthetic PII text and speech. The data covers the 40 supported languages, with the largest share in English, as well as code-switched speech.
#### [](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#training-data-size-details) Training Data Size Details
**Total examples, audio hours, text tokens, unique dataset count, partition percentages, and collection periods:**
- Total multimodal tokens (non-unique): 479 billion
- Audio share of multimodal input positions: approx. 60%
- Audio tokens (non-unique): 286 billion
- Text tokens (non-unique, including responses to audio examples): 193 billion
- Audio examples (unique indexed records, estimated): 372,206,279
- Audio examples (non-unique, estimated): 1,780,012,724
- Audio hours (unique indexed records, estimated): 1,718,453
- Audio hours (non-unique, estimated): 6,527,883
Audio figures estimate cumulative training exposure, excluding text-only data and pretraining of the base language model and audio encoder. “Unique” counts each indexed record within a source once; the same recording can appear in multiple records or sources, so these are not deduplicated recording hours.
**Top-level training dataset families:**
- Granary v2: ASR, translation, long-form, contextual biasing, audio QA, transcription style, language identification data
- Multi-speaker transcription and associated QA/timestamp variants
- Audio-understanding data
- Non-speech vocalization and speaking-style data
- No-speech abstention data
- Text-only instruction-tuning data
- PII-aware synthetic ASR data
- Nemotron-PII text data
**Processing:** Source manifests provide audio references, labels, conversations, and task metadata. The mixture includes human-labeled and pseudo-labeled transcriptions and translations with corresponding instruction prompts. Task-preference sampling selects among transcription, contextual transcription, speech QA, text normalization, captioning, and code-switched targets.
**Harmful-content processing:** The training mixture includes text and audio safety-tuning subsets and selected filtered or human-curated components.
**Public Summary of Training Content:**
- **Granary v2 multilingual speech data:** multilingual data covering ASR, speech translation, contextual and code-switched ASR, speech QA, ITN, captioning, long-form transcription, language identification, PII-aware ASR, and transcript-plus-QA/translation variants.
- **Multi-speaker ASR data:** meeting, conversational, and synthetic multi-speaker mixtures, including source material from AISHELL-4, AliMeeting, AMI, ICSI, DiPCo, Fisher/FisherHQ, and Multilingual LibriSpeech.
- **Non-speech vocalization and speaking-style annotation data:** used for paralinguistic event and style-token prediction.
- **Audio understanding data:** sound, music, and speech understanding; audio reasoning; spoken instruction following; and safety tuning. Its source families include AudioSet, AudioCaps, Clotho, CochlScene, FSD50K, VGGSound, MusicCaps, NSynth, MELD, MSP-Podcast, CREMA-D, and related audio-instruction mixtures.
- **Text-only instruction-tuning data:** instruction following, reasoning, coding, tool use, multilingual, and safety subsets.
- **PII-focused data:** Nemotron-PII text SFT plus synthetic PII-aware ASR
- **No-speech abstention data:** derived from AudioSet and non-speech audio understanding training data
Transcribe's training data includes data licensed from David AI. The DAI-ASR-I18N evaluation set was not part of it.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#public-datasets) Public Datasets
The Nemotron-3.5-Transcribe training recipe uses public source datasets both directly and through NVIDIA-created derivatives, including AISHELL-4, AliMeeting, AMI Meeting Corpus, DiPCo, Europarl, Google Speech Commands, ICSI Meeting Corpus, Libri-Light, LibriSpeech, MOSEL, Multilingual LibriSpeech, National Speech Corpus 1 and 6, ReazonSpeech v2, The People's Speech 12k, VCTK, VoxPopuli, and YTC.
Additional externally available source families used by the recipe include Granary v2, Common Voice 26, YODAS2, FLEURS, DUSHA, Golos, M-AILABS, Falar, SOVA, AudioSet, AudioCaps, Clotho, CochlScene, FSD50K, VGGSound, MusicCaps, NSynth, MELD, MSP-Podcast, and CREMA-D.
Nemotron-PII is an NVIDIA-produced synthetic dataset with a public dataset card.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#private-datasets) Private Datasets
The training mixture includes the following third-party, vendor-provided, or internally curated source families:
- **Third-party speech corpora:** Fisher/FisherHQ, Switchboard, Wall Street Journal, and SPGISpeech
- **Vendor-provided multilingual and multi-speaker data:** DavidAI D12a/D12b/D12c and MLC-SLM-2
- **Internal derivative datasets:** multi-speaker task variants, non-speech vocalization and speaking-style annotations, PII-aware ASR data, language-identification data, and no-speech abstention data
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#data-crawling-and-scraping) Data Crawling and Scraping
Some upstream source families are web-derived. These include YODAS/YODAS2 and YTC, as well as publicly available internet scale data represented in AudioSet and related audio-understanding and no-speech datasets. The audio understanding mixture and the pretrained base components may also contain web-derived data.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#internally-generated-data-including-prompt-collected-data) Internally Generated Data, Including Prompt-Collected Data
NVIDIA-generated or NVIDIA-transformed training artifacts include:
- Granary v2 pseudo-transcriptions and model-generated translations, together with human-improved and Riva-labeled variants
- Prompt-controlled Granary v2 task expansions for transcription, contextual transcription, speech QA, inverse text normalization, disfluency-reduced transcription, captioning, and code-switched transcription
- Fine-, coarse-, distractor-, partial-, and negative-context variants for contextual ASR
- Long-form transcription data and transcript-plus-QA, timestamp, speaker-tag, and translation variants
- Multi-speaker task formatting and constructed audio mixtures
- Non-speech vocalization and speaking-style annotations
- Balanced language-identification prompts and targets
- PII tagging and redaction examples
- No-speech abstention prompts and empty-response targets
- Curated Nemotron text SFT data, audio-instruction, audio-reasoning, and safety-tuning blends
Training prompts are developer-authored or generated task instructions associated with prepared training examples. User interactions with a deployed product or model were not used to train the model.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#self-sourced-synthetic-data) Self-Sourced Synthetic Data
Synthetic and model-generated training data include:
- **Nemotron-PII text data:** fully synthetic text examples covering more than 55 PII/PHI categories, with tagged and redacted outputs
- **PII-aware ASR data:** synthetic multilingual PII-positive speech examples
- **Constructed multi-speaker mixtures:** AuMix examples combining source recordings into conversations of up to eight speakers, including long-form mixtures
- **Granary v2 task variants:** generated contextual terms and distractors, speech questions and answers, captions, code-switched targets, and transcript-plus-QA/translation variants
- **Audio understanding synthetic-data-generation subsets:** generated captions, questions, answers, instruction-following targets, and reasoning traces for sound, music, speech, and audio-reasoning tasks
- **Prompt augmentation:** deterministic and generated prompt variants for transcription, language identification, PII handling, and no-speech abstention
Pseudo-labeled ASR and translation examples retain source audio and use model-generated labels; they should not be described as synthetic audio. Constructed multi-speaker mixtures likewise combine source recordings rather than generating new speaker identities.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#testing-dataset) Testing Dataset
**Data Collection Method by dataset:** Not Applicable
**Labeling Method by dataset:** Not Applicable
**Properties:** Not Applicable - Testing uses the public benchmark test sets described under Evaluation Dataset; there is no separate held-out testing dataset.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#evaluation-dataset) Evaluation Dataset
**Data Collection Method by dataset:** Hybrid: Manually-Collected, Automated
**Labeling Method by dataset:** Hybrid: Manually-Labeled, Automatic/Sensors
**Properties:**
- **Data count:** more than 330,000 test examples from public benchmarks, including approximately 243,000 for English, multilingual, multi-speaker, and contextual transcription; 29,000 for language identification; and 57,000 for audio understanding, voice instruction following, and text-only reasoning. Speech translation, long-form, code-switched, and entity benchmarks are evaluated in addition.
- **Modalities:** audio with text instructions, references, and context; text-only for the text reasoning benchmarks.
- **Nature of the content:** read speech (LibriSpeech, Multilingual LibriSpeech, FLEURS, Common Voice), parliamentary speech (VoxPopuli), podcasts and online video (GigaSpeech), financial earnings calls (Earnings-22, SPGISpeech), meetings (AMI, NOTSOFAR-1, AISHELL-4, AliMeeting), telephone conversations (CALLHOME), interviews (Mixer 6), noisy and degraded recordings (URGENT 2024), code-switched conversations, spoken questions and instructions (VoiceBench, Spoken-MQA), and speech, sound, and music for audio understanding (MMAU, MMAR, AIR-Bench).
- **Linguistic characteristics:** 40 supported languages, with the largest share in English, including Indian-accented English and Hindi (Monsoon), Mandarin meetings, and Hindi–English, Bengali–English, and Mandarin–English code-switching; transcripts are scored both verbatim and in normalized written form, depending on the benchmark.
[](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#benchmark-results) Benchmark Results
Results are grouped by task. **↓ Lower is better; ↑ higher is better.** Error rates and accuracy are percentages unless another scale is shown. Expand the language and subset tables for the complete results.
English Single-Speaker ASR · Open ASR Long-Form Leaderboard · Multilingual ASR · Multispeaker ASR · Context ASR · ASR Robustness · Language Identification · Translation · Audio and voice understanding · Text reasoning
**Evaluation settings.** Transcription, translation, and language identification use reasoning disabled and temperature 0. Audio/voice understanding and text reasoning use reasoning enabled, temperature 0.6, top-p 0.95, top-k disabled, and seed 0. The reasoning preset allows 20,480 completion tokens, including a 16,384-token reasoning budget and 1,024 additional tokens to finish reasoning. The context limit is 65,536 tokens. Task instructions are listed under Evaluation Prompts.
#### [](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#english-single-speaker-asr) English Single-Speaker ASR
**English Open ASR Leaderboard.** The overall result is the equal-weight mean of the nine datasets below.
| Dataset / condition | WER (%) ↓ | | --- | --- | | Nine-dataset mean | 4.71 | | AMI | 7.81 | | Earnings22 — Open ASR subset | 5.29 | | GigaSpeech | 7.18 | | LibriSpeech test-clean | 1.09 | | LibriSpeech test-other | 2.60 | | Monsoon — English (India) | 3.65 | | SPGISpeech | 1.91 | | URGENT24 | 11.00 | | VoxPopuli — Open ASR subset | 1.84 |
**Structured-content transcription.** These tests check numbers and exact values such as email addresses, file paths, and identifiers.
| Benchmark | Content | WER (%) ↓ | | --- | --- | --- | | Numb3rs | Number transcription | 3.55 | | VoiceCodeBench | Structured values | 8.38 |
| VoiceCodeBench measure | Result (%) ↑ | | --- | --- | | Canonical Token/Entity Match (CTEM) | 88.53 | | Task Success Rate (TSR) | 59.33 |
CTEM measures recovery of the target values; TSR measures the share of recordings in which every target value is correct. See the VoiceCodeBench metric definitions.
#### [](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#open-asr-long-form-leaderboard) Open ASR Long-Form Leaderboard
**Long-form Open ASR Leaderboard.** Recordings are processed in 55-minute chunks with five seconds of overlap. The overall result is the equal-weight mean of CORAAL, Earnings21, and Earnings22; CORAAL is averaged across its locations.
| Benchmark | WER (%) ↓ | | --- | --- | | Open ASR Leaderboard — long-form mean | 10.98 | | CORAAL — mean across locations | 13.39 | | Earnings21 | 7.57 | | Earnings22 | 11.99 |
CORAAL — results by location
| CORAAL location | WER (%) ↓ | | --- | --- | | Atlanta | 13.17 | | Detroit | 14.94 | | Lower East Side, New York | 12.94 | | Princeville | 21.06 | | Rochester | 8.17 | | Valdosta | 10.34 | | Washington, D.C. (DCA) | 14.66 | | Washington, D.C. (DCB) | 11.84 |
#### [](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#multilingual-asr) Multilingual ASR
| Benchmark | Measure | Result ↓ | | --- | --- | --- | | Multilingual Open ASR Leaderboard | Reported overall WER (%) | 4.62 |
The multilingual leaderboard covers FLEURS, Common Voice, Multilingual LibriSpeech, and Monsoon. Standalone FLEURS uses WER or CER depending on the language, while the leaderboard uses WER throughout. Reference transcripts and normalization of accents and compound words also differ, so the scores are not directly comparable.
Multilingual transcription — all language and dataset results **FLEURS and CoVoST2.** CER is used for Chinese, Japanese, Korean, Thai, and Vietnamese; WER is used for the other listed languages. A dash means no result is reported for that benchmark and language.
| Language | Error measure (%) ↓ | FLEURS | CoVoST2 | | --- | --- | --- | --- | | Arabic | WER | 6.35 | 11.67 | | Bulgarian | WER | 8.01 | — | | Chinese | CER | 8.62 | 9.74 | | Croatian | WER | 8.31 | — | | Czech | WER | 5.14 | — | | Danish | WER | 9.11 | — | | Dutch | WER | 3.75 | 5.10 | | English | WER | 3.35 | 6.72 | | Estonian | WER | 9.01 | 15.26 | | Filipino | WER | 18.28 | — | | Finnish | WER | 6.27 | — | | French | WER | 3.16 | 4.83 | | German | WER | 2.73 | 2.98 | | Greek | WER | 26.93 | — | | Hebrew | WER | 20.97 | — | | Hindi | WER | 7.47 | — | | Hungarian | WER | 7.66 | — | | Indonesian | WER | 14.51 | 42.26 | | Italian | WER | 1.41 | 3.09 | | Japanese | CER | 4.42 | 10.61 | | Korean | CER | 2.78 | — | | Latvian | WER | 8.37 | 18.50 | | Lithuanian | WER | 12.47 | — | | Maltese | WER | 16.41 | — | | Norwegian | WER | 63.76 | — | | Polish | WER | 3.81 | — | | Portuguese | WER | 3.01 | 3.01 | | Romanian | WER | 6.02 | — | | Russian | WER | 3.80 | 2.47 | | Slovak | WER | 5.28 | — | | Slovenian | WER | 10.90 | 33.53 | | Spanish | WER | 2.20 | 2.89 | | Swedish | WER | 7.27 | 13.63 | | Thai | CER | 7.34 | — | | Turkish | WER | 6.75 | 19.27 | | Ukrainian | WER | 5.88 | — | | Vietnamese | CER | 2.90 | — |
**Multilingual Open ASR Leaderboard.** Languages are listed alphabetically, with their mean and component datasets on the same row. Common Voice and Multilingual LibriSpeech are written out in full. Hindi uses orthographically informed WER (OIWER). The overall score is the equal-weight mean of the seven language means; each language mean equally averages its component dataset scores.
| Language | Measure | Language mean ↓ | FLEURS | Common Voice ↓ | Multilingual LibriSpeech ↓ | Monsoon ↓ | | --- | --- | --- | --- | --- | --- | --- | | Dutch | WER (%) | 5.96 | 3.94 | 5.49 | 8.45 | — | | French | WER (%) | 3.92 | 3.35 | 5.27 | 3.14 | — | | German | WER (%) | 2.81 | 2.62 | 2.99 | — | — | | Hindi | OIWER (%) | 9.15 | — | — | — | 9.15 | | Italian | WER (%) | 3.61 | 1.41 | 3.21 | 6.21 | — | | Portuguese | WER (%) | 4.06 | 3.13 | — | 4.99 | — | | Spanish | WER (%) | 2.82 | 2.33 | 3.15 | 2.98 | — |
**Code-switching and Indian speech.** These tests cover mixed-language speech and Hindi.
| Benchmark | Languages / condition | Measure | Result ↓ | | --- | --- | --- | --- | | ASCEND | Mandarin–English | Mixed error rate (%) | 18.74 | | MUCS 2021 | Bengali–English | WER (%) | 35.46 | | MUCS 2021 | Hindi–English | WER (%) | 19.28 | | Monsoon | Hindi (India) | Orthographically informed WER (OIWER) (%) | 9.40 |
#### [](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#multispeaker-asr) Multispeaker ASR
These tests measure both transcription and speaker assignment. **cpWER** matches predicted speaker labels to reference speakers before computing word error rate. The results below use full recordings.
| Benchmark | Recording condition | cpWER (%) ↓ | | --- | --- | --- | | AMI Meeting Corpus | Individual headset microphones (IHM) | 10.29 | | AMI Meeting Corpus | Single distant microphone (SDM) | 17.34 | | CALLHOME American English | CH109 test subset | 11.50 | | Mixer 6 | Channel 4 test recordings | 14.80 | | NOTSOFAR-1 | Individual headset microphones (IHM) | 14.94 | | NOTSOFAR-1 | Single-channel distant microphone (SC) | 25.91 |
Character error rate measures transcription errors in Mandarin; **cpCER** also accounts for speaker assignment.
| Benchmark | cpCER (%) ↓ | | --- | --- | | AISHELL-4 | 32.91 | | AliMeeting | 34.95 |
#### [](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#context-asr) Context ASR
Contextual tests supply vocabulary or topic hints.
| Benchmark | Context | WER (%) ↓ | | --- | --- | --- | | ContextASR-Bench | None | 3.33 | | ContextASR-Bench | Coarse | 3.34 | | ContextASR-Bench | Fine | 1.71 | | Contextual Earnings22 | None | 12.54 | | Contextual Earnings22 | Global | 10.28 | | Contextual Earnings22 | Local | 9.58 | | Preference-ASR | None | 7.56 | | Preference-ASR | Fine | 6.16 |
Contextual transcription — entity and keyword details
| ContextASR-Bench context | Named-entity WER (%) ↓ | Named-entity false-negative rate (%) ↓ | | --- | --- | --- | | None | 12.21 | 14.99 | | Coarse | 12.26 | 15.05 | | Fine | 2.20 | 1.32 |
| Contextual Earnings22 context | Keyword precision (%) ↑ | Keyword recall (%) ↑ | Keyword F1 (%) ↑ | | --- | --- | --- | --- | | None | 98.06 | 58.16 | 73.01 | | Global | 97.80 | 89.50 | 93.47 | | Local | 98.16 | 96.05 | 97.09 |
#### [](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#asr-robustness) ASR Robustness
| Benchmark | WER (%) ↓ | | --- | --- | | HALAS | 31.50 |
#### [](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#language-identification) Language Identification
**FLEURS** and **VoxLingua107** measure whether the model identifies the language spoken in a recording. Scores are reported per language, with no aggregate.
Language identification — accuracy for every evaluated language
| Language | FLEURS | VoxLingua107 | | --- | --- | --- | | Arabic | 99.77 | 97.00 | | Bulgarian | 99.70 | — | | Chinese | 100.00 | 100.00 | | Croatian | 99.56 | 100.00 | | Czech | 99.45 | — | | Danish | 99.89 | 98.99 | | Dutch | 100.00 | 100.00 | | English | 99.69 | 98.75 | | Estonian | 98.54 | 88.24 | | Filipino | 97.72 | — | | Finnish | 100.00 | 96.77 | | French | 100.00 | 99.00 | | German | 100.00 | 100.00 | | Greek | 97.69 | 100.00 | | Hebrew | 99.37 | — | | Hindi | 94.02 | — | | Hungarian | 99.12 | 100.00 | | Indonesian | 99.85 | — | | Italian | 100.00 | 100.00 | | Japanese | 100.00 | 100.00 | | Korean | 100.00 | — | | Latvian | 80.49 | 81.05 | | Lithuanian | 99.70 | 84.62 | | Maltese | 99.78 | — | | Polish | 99.87 | 100.00 | | Portuguese | 100.00 | 100.00 | | Romanian | 99.55 | — | | Russian | 99.74 | 100.00 | | Slovak | 97.85 | — | | Slovenian | 90.05 | 55.56 | | Spanish | 100.00 | 100.00 | | Swedish | 99.74 | 98.00 | | Thai | 100.00 | — | | Turkish | 100.00 | 95.83 | | Ukrainian | 100.00 | 100.00 | | Vietnamese | 100.00 | — |
All scores in this table are accuracy percentages (↑). A dash means the language was not evaluated in that benchmark. FLEURS uses its locale-specific test data; VoxLingua107 uses its language-labelled test data.
#### [](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B#speech-translation) Speech Translation
**FLEURS** and **CoVoST2** measure translation between spoken audio and written text. Each language direction is reported separately using BLEU or spBLEU (↑); no aggregate is shown.
Speech translation — BLEU/spBLEU for every evaluated language direction
| Language | FLEURS → English | English → FLEURS | CoVoST2 → English | English → CoVoST2 | | --- | --- | --- | --- | --- | | Arabic | 30.01 | 21.77 | 42.61 | 21.34 | | Bulgarian | 35.09 | 34.00 | — | — | | Chinese | 21.88 | 38.51 | 20.54 | 38.45 | | Croatian | 32.80 | 25.98 | — | — | | Czech | 35.07 | 27.33 | — | — | | Danish | 38.70 | 39.32 | — | — | | Dutch | 29.74 | 25.47 | 44.34 | — | | Estonian | 31.14 | 21.62 | 20.55 | 24.71 | | Filipino | 6.40 | 9.46 | — | — | | Finnish | 28.57 | 20.93 | — | — | | French | 38.19 | 42.51 | 41.14 | — | | German | 39.96 | 33.93 | 39.44 | 34.29 | | Greek | 24.44 | 23.78 | — | — | | Hebrew | 28.46 | 25.47 | — | — | | Hindi | 29.62 | 22.09 | — | — | | Hungarian | 28.33 | 20.69 | — | — | | Indonesian | 8.83 | 18.44 | 6.95 | 15.39 | | Italian | 29.10 | 27.14 | 38.77 | — | | Japanese | 23.00 | 29.73 | 28.93 | 29.92 | | Korean | 24.23 | 27.68 | — | — | | Latvian | 31.06 | 25.60 | 25.69 | 23.60 | | Lithuanian | 25.44 | 21.03 | — | — | | Maltese | 31.76 | 23.51 | — | — | | Norwegian | 29.74 | 17.24 | — | — | | Polish | 26.54 | 18.56 | — | — | | Portuguese | 44.33 | 42.65 | 54.46 | — | | Romanian | 36.76 | 34.01 | — | — | | Russian | 32.73 | 25.83 | 49.31 | — | | Slovak | 33.72 | 26.48 | — | — | | Slovenian | 26.83 | 23.54 | 22.46 | 29.49 | | Spanish | 28.70 | 25.60 | 41.70 | — | | Swedish | 39.79 | 38.61 | 40.51 | 38.97 | | Thai | 20.19 | 39.20* | — | — | | Turkish | 23.66 | 8.70 | 21.31 | 7.20 | | Ukrainian | 34.12 | 24.18 | — | — | | Vietnamese | 22.21 | 24.81 | — | — |
- The English → Thai FLEURS score uses spBLEU. All other scores in this table use BLEU.
“FLEURS → English” and “CoVoST2 → English” mean speech in the listed language translated into English text. The reverse columns translate English speech into text in the listed language. A dash means that direction was not eval