Source: https://ailoot.ai/models/en/microsoft-mai-transcribe-1-5/ Canonical: https://ailoot.ai/models/en/microsoft-mai-transcribe-1-5/ Language: en # Microsoft AI: MAI-Transcribe 1.5 [Markdown](https://ailoot.ai/models/en/microsoft-mai-transcribe-1-5/index.md) · [JSON](https://ailoot.ai/models/en/microsoft-mai-transcribe-1-5/facts.json) ## [Microsoft AI: MAI-Transcribe 1.5](https://ailoot.ai/models/en/microsoft-mai-transcribe-1-5/) Microsoft AI MAI-Transcribe 1.5 converts recorded speech into text across 43 languages, with automatic language identification and an emphasis on accents and noisy audio. Its model card describes domain-aware keyword biasing for names and specialist terms. Use the transcript as a basis for checked notes or captions. - [**Ailoot Ultra · Transcription** `POST /v1/audio/transcriptions` `microsoft/mai-transcribe-1.5`](https://ailoot.ai/models/en/microsoft-mai-transcribe-1-5/#method-0) About the model This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below. Microsoft AI MAI-Transcribe 1.5 is a speech-to-text model for converting recorded audio into written content. Its exact model card describes recognition across 43 languages, automatic language identification and robustness across accents, dialects, speaking styles and noisy conditions. The edition expands the 25-language set of MAI-Transcribe 1 by eighteen languages. A useful result is the written speech from a lecture, an interview, a call or a voice note, ready to check against the recording before a later editing or analysis step. Prepare a complete, intelligible recording in one of the maker’s listed formats: WAV, MP3 or FLAC. Keep the original file and any division into sections so the transcript can be compared with the audio. For a lecture, retain the phrases around a technical term rather than extracting only the difficult word. For a voice note, preserve a correction that changes a name, time or number. The surrounding speech provides context for recognizing what was said, and the listener’s review can identify where the output needs correction. Automatic language identification lets a recording be processed without requiring a language choice in advance. The 43-language set includes English, French, German, Italian, Spanish, Hindi, Chinese, Japanese, Korean, Arabic, Indonesian, Russian, Thai and Vietnamese. The added languages include Bulgarian, Catalan, Greek, Ukrainian and several South Asian languages. Check that the intended language belongs to this edition’s set rather than borrowing the broader coverage of MAI-Transcribe 2. For multilingual collections, retain the recording’s language with the reviewed text so downstream editing follows the actual spoken content. The 1.5 model card describes keyword or entity biasing with up to 200 terms, and its release explains the purpose: make likely names, acronyms and specialist words available as context. The maker describes contextual hints rather than blindly forcing supplied words into the result. Prepare a relevant vocabulary, such as the project name, a technical instrument or the correct spelling of a person discussed in the recording. Use that terminology as context for the actual recording, and check the recognized terms against the speech. For a recorded engineering lesson, prepare the names of the instruments discussed and the exact spelling of any project acronym. Write their exact spellings in a focused terminology list and keep the list alongside the audio and transcript. Review those occurrences against the spoken passages rather than treating a vocabulary entry as proof of a match. Names that sound similar can still need checking. The result should preserve the words actually spoken, including a correction or qualification that changes their meaning. The manufacturer emphasizes performance on real-world audio, including background noise and diverse microphone conditions. Practical review can focus on passages with simultaneous speech, faint voices or words partly covered by another sound. Keep uncertainties tied to the corresponding audio passage instead of filling them from assumptions about the topic. A plausible sentence may still contain the wrong proper noun or number. For a call transcript, the useful deliverable is a checked record of the spoken content that a later analysis can use, rather than a guessed account of the caller’s intent. The exact 1.5 model card lists a maximum file size of 300 MB and two hours. Its linked synchronous transcription REST reference describes a stricter serving input: smaller than 250 MB and shorter than two hours. These are separately stated model-card and interface limits. Prepare files within the allowance of the intended interface and organize longer material at meaningful recording boundaries. A file-size limit concerns the encoded audio, while the duration limit concerns the speech recording’s length; neither defines a text-model context window. The model card’s sample output is JSON containing the recording duration and combined recognized text. The text can become an accessible transcript, a script for captions or an input for later summarization and search. Caption cue grouping and alignment to the recording are separate preparation when the result does not include the required timing data. For interview records, attributing a line to a named participant needs a separate checked record of who spoke. Preserve the recognized wording and the audio as the basis for those later artifacts. The edition’s boundaries matter when planning the output. The 1.5 card explicitly places speaker diarization outside its supported capabilities, and the maker’s version comparison assigns word-level timestamps and clean/verbatim style controls to MAI-Transcribe 2. A native streaming model is also a separate edition. This 1.5 profile therefore centres on recognized text from recordings and its documented domain context, without turning another edition’s speaker, timing or live-input features into part of the result. Review names, numbers, technical terms and unclear speech before preparing a quoted passage, a caption track or a derived document. **Ailoot Ultra · Transcription** `POST /v1/audio/transcriptions` `microsoft/mai-transcribe-1.5` [`microsoft/mai-transcribe-1.5`](https://ailoot.ai/models/en/#m=microsoft%2Fmai-transcribe-1.5&route=Ultra&caller=microsoft%2Fmai-transcribe-1.5&fmt=transcriptions) Microsoft AI /`microsoft/mai-transcribe-1.5` ### Overview Microsoft AI MAI-Transcribe 1.5 converts recorded speech into text across 43 languages, with automatic language identification and an emphasis on accents and noisy audio. Its model card describes domain-aware keyword biasing for names and specialist terms. Use the transcript as a basis for checked notes or captions. Accepted inputs Audio Results Text ### Available in Ailoot · inputs & methods **Ailoot Ultra · Transcription** `POST /v1/audio/transcriptions` Input types `audio` Output types `text` | Field | Type | Requirement | Values & limits | Default | | --- | --- | --- | --- | --- | | Field`model` | Type`string` | RequirementRequired | Values & limits microsoft/mai-transcribe-1.5 | Default— | | Field`input_audio` | Type`object` | RequirementRequired | Values & limits | Default— | | Field`language` | Type`string` | RequirementOptional | Values & limits | Default— | | Field`temperature` | Type`number` | RequirementOptional | Values & limits Minimum: 0 Maximum: 1 | Default— | | Field`response_format` | Type`string` | RequirementOptional | Values & limits json, verbose\_json | Defaultjson | | Field`timestamp_granularities` | Type`array` | RequirementOptional | Values & limits | Default— | | Field`provider` | Type`object` | RequirementOptional | Values & limits | Default— | Method parameters and lifecycle | Parameter | Contract | | --- | --- | | Parameter`content_type` | Contractapplication/json | | Parameter`required` | Contractmodel, input\_audio | | Parameter`material_types` | Contractaudio | | Parameter`material_conditions` | ContractRead an actual WAV file and encode its real bytes for the declared JSON input\_audio; no multipart route is advertised for this exact JSON-only declaration. | Lifecycle: `synchronous_result_then_owned_library` `GET /v1/media/tasks/{task_id}` · actual returned task\_id or media library record `GET /v1/media/assets/{asset_id}/content` `GET /v1/media/assets` `GET /v1/media/assets/{asset_id}` ### Pricing ##### How the final charge is calculated The displayed rates estimate the cost before generation. The final charge is calculated from the cost recorded for the original generation. Measured output duration and forecast quantities are not the final billable quantity. Tariff references do not add request options. Supported parameters are listed for the selected method. Fast mode Not specified When fast mode is selected Flex mode Not specified When flex mode is selected Maximum reasoning effort Not specified When maximum reasoning effort uses the default adjustment #### Estimated rates for comparison #### Prices by configuration Match each charge to the conditions shown. Use one rate for each set of alternative configurations. | Configuration | Rate schedule | Forecast quantity | Billing unit | Charge | | --- | --- | --- | --- | --- | | ConfigurationBase token rates | Rate scheduleBase rate | Forecast quantity— | Billing unit— | Charge Input · Input tokens · Estimate for comparison only **—** / 1K tokens Output · Output tokens · Estimate for comparison only **—** / 1K tokens Input · Cache read · Estimate for comparison only **—** / 1K tokens Input · Cache write · Estimate for comparison only **—** / 1K tokens Input · Cache write, 1 hour · Estimate for comparison only **—** / 1K tokens | | Configurationinput audio hour | Rate schedule— | Forecast quantity— | Billing unitper input audio hour | Charge Input · input audio hour · Estimate for comparison only **$0.72** per input audio hour | | ConfigurationEstimated generation cost | Rate schedule— | Forecast quantityone generation estimate | Billing unit— | Charge Output · Estimated generation cost · Estimate for comparison only **$0.0002** estimate before generation This estimate is not an additional charge or a fixed rate for the completed generation. | Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/) ### Example requests #### Ailoot Ultra · Transcription curl · exact native body and complete result ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" : "${AILOOT_AUDIO_FILE:?Set the path to your actual WAV recording}" python3 - <<'PY_REQUEST' import json,os from pathlib import Path import base64 audio=Path(os.environ['AILOOT_AUDIO_FILE']).read_bytes() if not (audio[:4]==b'RIFF' and audio[8:12]==b'WAVE'): raise SystemExit('Supply an actual WAV audio file') if len(audio)>26214400: raise SystemExit('Use a documented supported large-file flow') request={'model': 'microsoft/mai-transcribe-1.5', 'input_audio': {'data': '${BASE64_ACTUAL_WAV_BYTES}', 'format': 'wav'}} request['input_audio']['data']=base64.b64encode(audio).decode('ascii') Path('request.json').write_text(json.dumps(request,ensure_ascii=False),encoding='utf-8') PY_REQUEST curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/audio/transcriptions \ -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \ --data-binary @request.json -D response-headers.txt -o response.bin # Keep the complete original response and headers. Read every returned segment, word and usage field. # Preserve actual operation/task/asset IDs; continue reads of the original result without resubmitting. ``` ### Receive and save results #### Ailoot Ultra · Transcription - `GET /v1/media/tasks/{task_id}` - `GET /v1/media/assets/{asset_id}/content` - `GET /v1/media/assets` - `GET /v1/media/assets/{asset_id}` Other content reads Read existing owned assets ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/media/assets \ -H "Authorization: Bearer $AILOOT_API_KEY" -o owned-assets.json # Preserve every returned asset and follow its exact signed content URL. Do not construct a signed token. ``` ##### Keep all completed results Keep every complete returned response, file and content part. Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again. ##### If the original response needs checking Keep the returned X-Client-Request-ID for reconciliation. Read only actual returned task\_id/asset\_id values or the owned media library; never build a task URL from the operation ID and never resubmit creation. Response fields | Field | Type | | --- | --- | | Field`text` | Type | | Field`usage` | Type | | Field`segments[]` | Type | | Field`words[]` | Type | | Field`speaker fields` | Type | | Field`timestamps` | Type | ### Aliases #### Model aliases **Ailoot Ultra** [`microsoft/mai-transcribe-1.5`](https://ailoot.ai/models/en/#m=microsoft%2Fmai-transcribe-1.5&route=Ultra&caller=microsoft%2Fmai-transcribe-1.5&fmt=transcriptions) #### Method path aliases Use the method paths shown above. #### Related caller IDs [`microsoft/mai-transcribe-1.5`](https://ailoot.ai/models/en/#m=microsoft%2Fmai-transcribe-1.5&route=Ultra&caller=microsoft%2Fmai-transcribe-1.5&fmt=transcriptions) FAQ · Microsoft AI: MAI-Transcribe 1.5 What inputs and results are available? `POST /v1/audio/transcriptions` · `microsoft/mai-transcribe-1.5`: `audio` → `text` Which fields does the request use? `POST /v1/audio/transcriptions` · `microsoft/mai-transcribe-1.5`: `model`, `input_audio`, `language`, `temperature`, `response_format`, `timestamp_granularities`, `provider` What limits apply? `POST /v1/audio/transcriptions` · `microsoft/mai-transcribe-1.5`: `temperature.minimum`: `0`; `temperature.maximum`: `1` Connect Ailoot to Claude Code, OpenCode, Hermes or your own app. ## The company and its project portfolio The company and its project portfolio: millions of customers; monthly sales in the billions. ## Buy tokens at a discount. 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