bytedance/seed-asr-2.0

Audio-zu-Text

Seed ASR 2.0 API by ByteDance

bytedance/seed-asr-2.0

Seed-asr-2.0

BytePlus Seed Speech recording-file recognition (ASR 2.0): transcribe an audio file to text with punctuation, ITN, sentence segmentation, speaker diarization, and 51-language support.

Seed ASR 2.0 wurde von ByteDance entwickelt. Atlas Cloud (betrieben von Atlas Cloud AI LLC) stellt lediglich den Zugang bereit und ist nicht Eigentümerin des Modells. Alle Marken sind Eigentum ihrer jeweiligen Inhaber.

Eingabe

Audio *

Sie können Dateien hier ablegen oder zum Hochladen klicken

MAX:1

Format

Sprache

Zahlennormalisierung

Satzzeichen

Semantische Glättung

Sprechertrennung

Erweiterte Einstellungen

Ausgabe

Inaktiv

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Parameter

Codebeispiel
import requests
import time

# Step 1: Start audio generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateAudio"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "bytedance/seed-asr-2.0",  # Required. model name
    "audio_url": "example_value",  # Required. URL of the audio file to transcribe
    "format": "mp3",  # Audio container format. options: mp3 | wav | ogg | raw
    "language": "example_value",  # Optional language code
    "enable_itn": True,  # Inverse text normalization (e
    "enable_punc": False,  # Add punctuation to the transcript
    "enable_ddc": False,  # Semantic smoothing (remove fillers/repeats)
    "enable_speaker_info": False,  # Speaker diarization (<=10 speakers)
    "show_utterances": False,  # Return per-sentence segments with timestamps
    "boosting_table_name": "example_value",  # Hotword table name to boost recognition of listed terms (e
    "correct_table_name": "example_value",  # Correction table name: post-recognition forced replacement (wrong→right pairs)
    "context": "example_value",  # Inline hotwords, no table needed
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] in ["completed", "succeeded"]:
            print("Generated audio:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

audio_url = check_status()

Installieren

Installieren Sie das erforderliche Paket für Ihre Programmiersprache.

pip install requests

Authentifizierung

Alle API-Anfragen erfordern eine Authentifizierung über einen API-Schlüssel. Sie können Ihren API-Schlüssel über das Atlas Cloud Dashboard erhalten.

export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP-Header

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Schützen Sie Ihren API-Schlüssel

Geben Sie Ihren API-Schlüssel niemals in clientseitigem Code oder öffentlichen Repositories preis. Verwenden Sie stattdessen Umgebungsvariablen oder einen Backend-Proxy.

Anfrage senden

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateAudio"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "text": "Hello, welcome to AtlasCloud text-to-speech."
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

Anfrage senden

Senden Sie eine asynchrone Generierungsanfrage. Die API gibt eine Vorhersage-ID zurück, mit der Sie den Status prüfen und das Ergebnis abrufen können.

POST/api/v1/model/generateAudio

Anfragekörper

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateAudio"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "bytedance/seed-asr-2.0",
    "text": "Hello, welcome to AtlasCloud text-to-speech."
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['data']['id']}")
print(f"Status: {result['data']['status']}")

Antwort

{
  "code": 200,
  "data": {
    "id": "pred_abc123",
    "status": "processing",
    "model": "model-name",
    "created_at": "2025-01-01T00:00:00Z"
  }
}

Status prüfen

Fragen Sie den Vorhersage-Endpunkt ab, um den aktuellen Status Ihrer Anfrage zu überprüfen.

GET/api/v1/model/prediction/{prediction_id}

Abfrage-Beispiel

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

Statuswerte

processingDie Anfrage wird noch verarbeitet.

completedDie Generierung ist abgeschlossen. Ergebnisse sind verfügbar.

succeededDie Generierung war erfolgreich. Ergebnisse sind verfügbar.

failedDie Generierung ist fehlgeschlagen. Überprüfen Sie das Fehlerfeld.

Abgeschlossene Antwort

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.mp3"
    ],
    "metrics": {
      "predict_time": 8.3
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

Eingabe-Schema

Die folgenden Parameter werden im Anfragekörper akzeptiert.

Gesamt: 12Erforderlich: 2Optional: 10

modelstringrequired

model name

Default: "bytedance/seed-asr-2.0"

audio_urlstringrequired

URL of the audio file to transcribe. Public URL or Base64. Formats: wav/mp3/ogg/raw. Max ~512MB.

formatstring

Audio container format.

Default: "mp3"

mp3wavoggraw

languagestring

Optional language code. Leave empty to auto-recognize Chinese/English/dialects. Or set a code, e.g. en-US, zh-CN, yue-CN, ja-JP, es-MX (51 languages supported).

enable_itnboolean

Inverse text normalization (e.g. 'one hundred' -> '100').

Default: true

enable_puncboolean

Add punctuation to the transcript.

Default: false

enable_ddcboolean

Semantic smoothing (remove fillers/repeats).

Default: false

enable_speaker_infoboolean

Speaker diarization (<=10 speakers).

Default: false

show_utterancesboolean

Return per-sentence segments with timestamps.

Default: false

boosting_table_namestring

Hotword table name to boost recognition of listed terms (e.g. product names). Tables are provisioned server-side by Atlas — contact us with your word list to get a table name. NOTE: a nonexistent table name is silently ignored (the task still succeeds).

correct_table_namestring

Correction table name: post-recognition forced replacement (wrong→right pairs). Provisioned server-side by Atlas, same as boosting_table_name. A nonexistent table name is silently ignored.

contextstring

Inline hotwords, no table needed. Must be a JSON STRING of the form {"hotwords":[{"word":"YourTerm"}]}. WARNING: plain non-JSON text makes the task FAIL; unrecognized JSON keys are silently ignored.

Beispiel-Anfragekörper

{
  "model": "bytedance/seed-asr-2.0",
  "audio_url": "example_audio_url",
  "format": "mp3",
  "enable_itn": true,
  "enable_punc": false,
  "enable_ddc": false,
  "enable_speaker_info": false,
  "show_utterances": false
}

Ausgabe-Schema

Die API gibt eine Vorhersage-Antwort mit den generierten Ausgabe-URLs zurück.

modelstring

model name

Default: "bytedance/seed-asr-2.0"

created_atstring

ISO timestamp of when the request was created.

idstring

Unique identifier for the prediction.

outputsarray

Array with the recognized transcript text (outputs[0]).

statusstring

Status: created, processing, completed, or failed.

stt_resultobject

Structured transcription result: text, duration (seconds), and words[] with per-word timestamps when show_utterances=true.

Beispielantwort

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.png"
  ],
  "metrics": {
    "predict_time": 8.3
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills integriert über 400 KI-Modelle direkt in Ihren KI-Programmierassistenten. Ein Befehl zur Installation, dann generieren Sie per natürlicher Sprache Bilder und Videos und chatten mit LLMs.

Unterstützte Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ unterstützte clients

Installieren

npx skills add AtlasCloudAI/atlas-cloud-skills

API-Schlüssel einrichten

Erhalten Sie Ihren API-Schlüssel über das Atlas Cloud Dashboard und setzen Sie ihn als Umgebungsvariable.

export ATLASCLOUD_API_KEY="your-api-key-here"

Funktionen

Nach der Installation können Sie natürliche Sprache in Ihrem KI-Assistenten verwenden, um auf alle Atlas Cloud Modelle zuzugreifen.

BildgenerierungGenerieren Sie Bilder mit Modellen wie Nano Banana 2, Z-Image und mehr.

VideoerstellungErstellen Sie Videos aus Text oder Bildern mit Kling, Vidu, Veo usw.

LLM-ChatChatten Sie mit Qwen, DeepSeek und anderen großen Sprachmodellen.

Medien-UploadLaden Sie lokale Dateien für Bildbearbeitung und Bild-zu-Video-Workflows hoch.

Mehr erfahren

github.com/AtlasCloudAI/atlas-cloud-skills

MCP-Server

Der Atlas Cloud MCP-Server verbindet Ihre IDE mit über 400 KI-Modellen über das Model Context Protocol. Funktioniert mit jedem MCP-kompatiblen Client.

Unterstützte Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ unterstützte clients

Installieren

npx -y atlascloud-mcp

Konfiguration

Fügen Sie die folgende Konfiguration zur MCP-Einstellungsdatei Ihrer IDE hinzu.

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Verfügbare Werkzeuge

atlas_generate_imageGenerieren Sie Bilder aus Textbeschreibungen.

atlas_generate_videoErstellen Sie Videos aus Text oder Bildern.

atlas_chatChatten Sie mit großen Sprachmodellen.

atlas_list_modelsDurchsuchen Sie über 400 verfügbare KI-Modelle.

atlas_quick_generateInhaltserstellung in einem Schritt mit automatischer Modellauswahl.

atlas_upload_mediaLaden Sie lokale Dateien für API-Workflows hoch.

Mehr erfahren

github.com/AtlasCloudAI/mcp-server

API-Schema

{
  "info": {
    "description": "The AtlasCloud API.",
    "title": "AtlasCloud API",
    "version": "1.0.0"
  },
  "paths": {
    "/api/v1/model/generateAudio": {
      "post": {
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        },
        "requestBody": {
          "required": true,
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          }
        }
      },
      "x-api-name": "model_run"
    },
    "/api/v1/model/prediction/{request_id}": {
      "get": {
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        },
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "schema": {
              "type": "string",
              "description": "Request ID"
            },
            "required": true
          }
        ]
      },
      "x-api-name": "model_result"
    }
  },
  "openapi": "3.0.0",
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ],
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "bytedance/seed-asr-2.0"
          },
          "audio_url": {
            "description": "URL of the audio file to transcribe. Public URL or Base64. Formats: wav/mp3/ogg/raw. Max ~512MB.",
            "type": "string",
            "x-ui-component": "uploaders"
          },
          "format": {
            "type": "string",
            "description": "Audio container format.",
            "default": "mp3",
            "enum": [
              "mp3",
              "wav",
              "ogg",
              "raw"
            ]
          },
          "language": {
            "description": "Optional language code. Leave empty to auto-recognize Chinese/English/dialects. Or set a code, e.g. en-US, zh-CN, yue-CN, ja-JP, es-MX (51 languages supported).",
            "type": "string"
          },
          "enable_itn": {
            "type": "boolean",
            "description": "Inverse text normalization (e.g. 'one hundred' -> '100').",
            "default": true
          },
          "enable_punc": {
            "type": "boolean",
            "description": "Add punctuation to the transcript.",
            "default": false
          },
          "enable_ddc": {
            "type": "boolean",
            "description": "Semantic smoothing (remove fillers/repeats).",
            "default": false
          },
          "enable_speaker_info": {
            "type": "boolean",
            "description": "Speaker diarization (<=10 speakers).",
            "default": false
          },
          "show_utterances": {
            "type": "boolean",
            "description": "Return per-sentence segments with timestamps.",
            "default": false
          },
          "boosting_table_name": {
            "type": "string",
            "description": "Hotword table name to boost recognition of listed terms (e.g. product names). Tables are provisioned server-side by Atlas — contact us with your word list to get a table name. NOTE: a nonexistent table name is silently ignored (the task still succeeds)."
          },
          "correct_table_name": {
            "type": "string",
            "description": "Correction table name: post-recognition forced replacement (wrong→right pairs). Provisioned server-side by Atlas, same as boosting_table_name. A nonexistent table name is silently ignored."
          },
          "context": {
            "type": "string",
            "description": "Inline hotwords, no table needed. Must be a JSON STRING of the form {\"hotwords\":[{\"word\":\"YourTerm\"}]}. WARNING: plain non-JSON text makes the task FAIL; unrecognized JSON keys are silently ignored."
          }
        },
        "required": [
          "model",
          "audio_url"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "audio_url",
          "format",
          "language",
          "enable_itn",
          "enable_punc",
          "enable_ddc",
          "enable_speaker_info",
          "show_utterances",
          "context",
          "boosting_table_name",
          "correct_table_name"
        ]
      },
      "PredictionResponse": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "bytedance/seed-asr-2.0"
          },
          "created_at": {
            "description": "ISO timestamp of when the request was created.",
            "format": "date-time",
            "type": "string"
          },
          "id": {
            "description": "Unique identifier for the prediction.",
            "type": "string"
          },
          "outputs": {
            "description": "Array with the recognized transcript text (outputs[0]).",
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "status": {
            "description": "Status: created, processing, completed, or failed.",
            "type": "string"
          },
          "urls": {
            "description": "Object containing related API endpoints.",
            "type": "object"
          },
          "stt_result": {
            "type": "object",
            "description": "Structured transcription result: text, duration (seconds), and words[] with per-word timestamps when show_utterances=true.",
            "properties": {
              "text": {
                "type": "string",
                "description": "Full transcript."
              },
              "duration": {
                "type": "number",
                "description": "Audio duration in seconds."
              },
              "words": {
                "type": "array",
                "description": "Word-level segments (show_utterances=true). Times in seconds.",
                "items": {
                  "type": "object",
                  "properties": {
                    "text": {
                      "type": "string"
                    },
                    "start": {
                      "type": "number"
                    },
                    "end": {
                      "type": "number"
                    },
                    "type": {
                      "type": "string",
                      "description": "word | utterance"
                    },
                    "speaker_id": {
                      "type": "string",
                      "description": "Present when enable_speaker_info=true."
                    }
                  }
                }
              }
            }
          }
        },
        "type": "object"
      }
    },
    "securitySchemes": {
      "apiKeyAuth": {
        "in": "header",
        "name": "Authorization",
        "type": "apiKey"
      }
    }
  }
}

LLM-freundliche Prompt-Vorlage

# bytedance/seed-asr-2.0

> BytePlus Seed Speech recording-file recognition (ASR 2.0): transcribe an audio file to text with punctuation, ITN, sentence segmentation, speaker diarization, and 51-language support.


## Overview

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateAudio` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `bytedance/seed-asr-2.0`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  model name
  - Default: `"bytedance/seed-asr-2.0"`

- **`audio_url`** (`string`, _required_):
  URL of the audio file to transcribe. Public URL or Base64. Formats: wav/mp3/ogg/raw. Max ~512MB.

- **`format`** (`string`, _optional_):
  Audio container format.
  - Default: `"mp3"`
  - Options: "mp3", "wav", "ogg", "raw"

- **`language`** (`string`, _optional_):
  Optional language code. Leave empty to auto-recognize Chinese/English/dialects. Or set a code, e.g. en-US, zh-CN, yue-CN, ja-JP, es-MX (51 languages supported).

- **`enable_itn`** (`boolean`, _optional_):
  Inverse text normalization (e.g. 'one hundred' -> '100').
  - Default: `true`

- **`enable_punc`** (`boolean`, _optional_):
  Add punctuation to the transcript.
  - Default: `false`

- **`enable_ddc`** (`boolean`, _optional_):
  Semantic smoothing (remove fillers/repeats).
  - Default: `false`

- **`enable_speaker_info`** (`boolean`, _optional_):
  Speaker diarization (<=10 speakers).
  - Default: `false`

- **`show_utterances`** (`boolean`, _optional_):
  Return per-sentence segments with timestamps.
  - Default: `false`

- **`context`** (`string`, _optional_):
  Inline hotwords, no table needed. Must be a JSON STRING of the form {"hotwords":[{"word":"YourTerm"}]}. WARNING: plain non-JSON text makes the task FAIL; unrecognized JSON keys are silently ignored.

- **`boosting_table_name`** (`string`, _optional_):
  Hotword table name to boost recognition of listed terms (e.g. product names). Tables are provisioned server-side by Atlas — contact us with your word list to get a table name. NOTE: a nonexistent table name is silently ignored (the task still succeeds).

- **`correct_table_name`** (`string`, _optional_):
  Correction table name: post-recognition forced replacement (wrong→right pairs). Provisioned server-side by Atlas, same as boosting_table_name. A nonexistent table name is silently ignored.



**Required Parameters Example**:

```json
{
  "model": "bytedance/seed-asr-2.0",
  "audio_url": ""
}
```


**Full Example**:

```json
{
  "model": "bytedance/seed-asr-2.0",
  "audio_url": "",
  "format": "mp3",
  "language": "",
  "enable_itn": true,
  "enable_punc": false,
  "enable_ddc": false,
  "enable_speaker_info": false,
  "show_utterances": false,
  "context": "",
  "boosting_table_name": "",
  "correct_table_name": ""
}
```


### Output Schema

The API returns the following output format:


- **`model`** (`string`, _optional_):
  model name
  - Default: `"bytedance/seed-asr-2.0"`

- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created.

- **`id`** (`string`, _optional_):
  Unique identifier for the prediction.

- **`outputs`** (`array[string]`, _optional_):
  Array with the recognized transcript text (outputs[0]).

- **`status`** (`string`, _optional_):
  Status: created, processing, completed, or failed.

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.

- **`stt_result`** (`object`, _optional_):
  Structured transcription result: text, duration (seconds), and words[] with per-word timestamps when show_utterances=true.
  - Properties:
    - **`text`** (`string`, _optional_):
      Full transcript.

    - **`duration`** (`number`, _optional_):
      Audio duration in seconds.

    - **`words`** (`array[object]`, _optional_):
      Word-level segments (show_utterances=true). Times in seconds.
      - Item properties:
        - **`text`** (`string`, _optional_):

        - **`start`** (`number`, _optional_):

        - **`end`** (`number`, _optional_):

        - **`type`** (`string`, _optional_):
          word | utterance

        - **`speaker_id`** (`string`, _optional_):
          Present when enable_speaker_info=true.





**Example Response**:

```json
{
  "model": "bytedance/seed-asr-2.0",
  "created_at": "",
  "id": "",
  "outputs": [
    ""
  ],
  "status": "",
  "urls": {},
  "stt_result": {
    "text": "",
    "duration": 0,
    "words": [
      {
        "text": "",
        "start": 0,
        "end": 0,
        "type": "",
        "speaker_id": ""
      }
    ]
  }
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateAudio" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "bytedance/seed-asr-2.0",
  "audio_url": "",
  "format": "mp3",
  "language": "",
  "enable_itn": true,
  "enable_punc": false,
  "enable_ddc": false,
  "enable_speaker_info": false,
  "show_utterances": false,
  "context": "",
  "boosting_table_name": "",
  "correct_table_name": ""
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/bytedance/seed-asr-2.0)

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Seed ASR 2.0 (Recording-File Recognition)

Transcribe an audio file to text with punctuation, ITN, sentence segmentation, speaker diarization, and 51-language support.

🌟 Key Features

🗣 High-Accuracy Transcription

State-of-the-art recognition tuned for real-world recordings.

🌍 51 Languages

Auto-detects Chinese/English/dialects, or pin a language code.

🎯 Rich Output

Punctuation, inverse text normalization, per-word timestamps, speaker labels.

📻 Far-Field Optimized

Robust to meeting-room and call-center audio.

⚙️ Parameters

Parameter	Required	Description
`audio_url`*	✅	Audio file URL or Base64. Formats: wav/mp3/ogg/raw.
`format`	⬜	Audio format: `mp3` (default) / `wav` / `ogg` / `raw`.
`language`	⬜	Language code (e.g. `en-US`, `yue-CN`); empty = auto (CN/EN/dialects). 51 languages.
`enable_itn`	⬜	Inverse text normalization. Default `true`.
`enable_punc`	⬜	Add punctuation. Default `false`.
`enable_ddc`	⬜	Semantic smoothing. Default `false`.
`enable_speaker_info`	⬜	Speaker diarization. Default `false`.
`show_utterances`	⬜	Per-word timestamps in `stt_result.words`. Default `false`.
`context`	⬜	Inline hotwords — no table needed. JSON string: `{"hotwords":[{"word":"YourTerm"}]}`. See below.
`boosting_table_name`	⬜	Server-side hotword table name (provisioned by Atlas).
`correct_table_name`	⬜	Server-side correction table name (wrong→right replacement, provisioned by Atlas).

🔥 Hotwords & Correction

Two ways to improve recognition of product names, people, and domain terms:

1. Inline via context (recommended — self-service, per request):

{
  "audio_url": "https://…/meeting.mp3",
  "context": "{\"hotwords\":[{\"word\":\"Zylophex\"},{\"word\":\"AtlasCloud\"}]}"
}

⚠️ context must be a JSON string exactly as above. Plain text (non-JSON) makes the task fail; unrecognized JSON keys are silently ignored.

2. Server-side tables via boosting_table_name / correct_table_name: for large or stable word lists. Tables live in the service account — send Atlas your word list and we provision the table and hand back its name. ⚠️ A nonexistent table name is silently ignored (the task still succeeds), so use the exact name we give you.

📤 Output

outputs[0] contains the recognized transcript text. stt_result carries the structured result: text, duration (seconds), and — with show_utterances=true — words[] with per-word start/end timestamps in seconds (plus speaker_id when diarization is on).

🎯 Use Cases

Meeting & Interview Transcription — turn recordings into searchable text.
Media Subtitling — transcribe podcasts/videos with timestamps.
Voice Analytics — call-center QA, speaker diarization.
Multilingual Content — transcribe across 51 languages.

Seed Audio 1.0

Doubao‑Audio‑Generate‑1.0 is Doubao Voice’s next‑generation audio‑generation engine. The industry‑first commercial tool creates film‑grade audio with just one prompt. It eliminates cumbersome audio‑engineering work. Creators generate publish‑ready radio dramas, podcasts and branded audio easily, shifting from a simple voice‑generator to an AI audio director. It serves audiobooks, serialized episodes and commercial audio for high‑quality narrative‑driven production.

MiniMax Music 3.0

MiniMax Music 3.0 is MiniMax's 11.1B-parameter open-weights music model that turns a musical description and optional lyrics into a complete, fully arranged and mixed song of up to five minutes - vocals, instrumentation and production included - in a single generation, with section-tag control over the arrangement and vocal or instrumental output.

MiniMax Lyrics Generation

MiniMax Lyrics Generation is a dedicated lyric-writing model that turns a one-line theme into a complete, professionally structured set of song lyrics - title, style tags, and sections marked with [Verse]/[Chorus] structure tags - and can also edit, continue, or restructure existing lyrics, with output directly usable as the lyrics input of MiniMax's music models.

Suno chirp-v4-5-all

Suno text-to-music via APIMart: inspiration mode (custom=false) turns a description into a song; custom mode (custom=true) uses your own lyrics. Async; returns 2 tracks per generation.

Suno chirp-v4-5-plus

Suno text-to-music via APIMart: inspiration mode (custom=false) turns a description into a song; custom mode (custom=true) uses your own lyrics. Async; returns 2 tracks per generation.

Suno chirp-auk

Suno text-to-music via APIMart: inspiration mode (custom=false) turns a description into a song; custom mode (custom=true) uses your own lyrics. Async; returns 2 tracks per generation.

Suno chirp-fenix

Suno text-to-music via APIMart: inspiration mode (custom=false) turns a description into a song; custom mode (custom=true) uses your own lyrics. Async; returns 2 tracks per generation.

Suno chirp-v3-5

Suno text-to-music via APIMart: inspiration mode (custom=false) turns a description into a song; custom mode (custom=true) uses your own lyrics. Async; returns 2 tracks per generation.

Suno chirp-v4

Suno text-to-music via APIMart: inspiration mode (custom=false) turns a description into a song; custom mode (custom=true) uses your own lyrics. Async; returns 2 tracks per generation.

Suno chirp-v5

Suno text-to-music via APIMart: inspiration mode (custom=false) turns a description into a song; custom mode (custom=true) uses your own lyrics. Async; returns 2 tracks per generation.

xAI TTS v1

xAI TTS v1 is a high-fidelity text-to-speech model that converts text into natural, expressive speech with sub-second latency, supporting 20 languages and 80+ voices with fine-grained delivery control.

From

$0.015/K chars

Text-zu-Audio

MiniMax Music 2.6

MiniMax text-to-music (latest): generate a full vocal or instrumental song from a style prompt plus lyrics with [Verse]/[Chorus] structure tags. Synchronous single-call generation.

ElevenLabs v3 Text-to-Speech

ElevenLabs v3 Text-to-Speech model. High-quality speech generation from text prompts.

xAI STT v1

xAI STT v1 is a production-grade speech-to-text model that transcribes audio into accurate, formatted text. It supports 24+ languages with automatic language detection, word-level timestamps, speaker diarization, multichannel transcription, and inverse text normalization.

From

$0.002/Minute

Eine API für alle Media-KI.

Alle Modelle erkunden

Seed ASR 2.0 API by ByteDance

Eingabe

Ausgabe

Parameter

Codebeispiel

Installieren

Authentifizierung

HTTP-Header

Anfrage senden

Anfrage senden

Anfragekörper

Antwort

Status prüfen

Abfrage-Beispiel

Statuswerte

Abgeschlossene Antwort

Eingabe-Schema

Beispiel-Anfragekörper

Ausgabe-Schema

Beispielantwort

Atlas Cloud Skills

Unterstützte Clients

Installieren

API-Schlüssel einrichten

Funktionen

MCP-Server

Unterstützte Clients

Installieren

Konfiguration

Verfügbare Werkzeuge

API-Schema

LLM-freundliche Prompt-Vorlage

Seed ASR 2.0 (Recording-File Recognition)

🌟 Key Features

🗣 High-Accuracy Transcription

🌍 51 Languages

🎯 Rich Output

📻 Far-Field Optimized

⚙️ Parameters

🔥 Hotwords & Correction

📤 Output

🎯 Use Cases

Ähnliche Modelle Erkunden

Seed Audio 1.0

MiniMax Music 3.0

MiniMax Lyrics Generation

Suno chirp-v4-5-all

Suno chirp-v4-5-plus

Suno chirp-auk

Suno chirp-fenix

Suno chirp-v3-5

Suno chirp-v4

Suno chirp-v5

xAI TTS v1

MiniMax Music 2.6

ElevenLabs v3 Text-to-Speech

xAI STT v1

Eine API für alle Media-KI.

Eingabe

Ausgabe

Parameter

Codebeispiel

Installieren

Authentifizierung

HTTP-Header

Anfrage senden

Anfrage senden

Anfragekörper

Antwort

Status prüfen

Abfrage-Beispiel

Statuswerte

Abgeschlossene Antwort

Eingabe-Schema

Beispiel-Anfragekörper

Ausgabe-Schema

Beispielantwort

Atlas Cloud Skills

Unterstützte Clients

Installieren