Use the Paraformer Python SDK to transcribe audio and video files with the DashScope API.
ImportantAlibaba Cloud Model Studio has released a workspace-specific domain for the China (Beijing) region. The new dedicated domain delivers superior performance and higher stability for inference requests. We recommend migrating from dashscope.aliyuncs.com to {WorkspaceId}.cn-beijing.maas.aliyuncs.com.
Replace {WorkspaceId} with your actual Workspace ID. The existing domain remains fully functional.
User guide:Non-real-time speech recognition
Prerequisites
You have activated the service and Obtain an API key. Please Configure API key as an environment variable instead of hardcoding it in your code to prevent security risks caused by code leakage.
NoteWhen you need to provide temporary access to third-party applications or users, or when you want to strictly control high-risk operations such as accessing or deleting sensitive data, we recommend using temporary authentication tokens.
Compared with long-term API Keys, temporary authentication tokens have a short validity period (60 seconds) and higher security, making them suitable for temporary call scenarios and effectively reducing the risk of API Key leakage.
Usage: In your code, replace the API Key originally used for authentication with the obtained temporary authentication token.
Getting started
The core resource (client.audio.asr) provides interfaces for async task submission, synchronous wait for task completion, and async result query (one-step transcribe, or step-by-step jobs.create + jobs.wait + jobs.fetch). You can use either of the following two calling approaches for non-real-time speech recognition:
- Async submission + sync wait: Submit a task and block until it completes and returns the result.
- Async submission + async polling: Submit a task and poll for results at any time.
Async submission + sync wait
-
Call the core resource (
client.audio.asr) (jobs.create/transcribe) and set the request parameters.Note
- The file transcription service processes tasks submitted through the API on a best-effort basis. After submission, a task enters the queued (
PENDING) state. The queue time depends on the queue length and file duration and cannot be precisely estimated, but typically completes within a few minutes. Once processing begins, speech recognition completes at several hundred times the real-time speed. - After each task completes, the recognition result and URL download link are valid for 24 hours. After expiration, you cannot query the task or download results through the previously provided URL.
- The file transcription service processes tasks submitted through the API on a best-effort basis. After submission, a task enters the queued (
-
Call the core resource (
client.audio.asr) (jobs.wait, or the one-steptranscribe) to wait synchronously for the task to complete.Task statuses:
PENDING,RUNNING,SUCCEEDED,FAILED. Thewaitcall blocks duringPENDINGorRUNNING. When the task reachesSUCCEEDEDorFAILED,waitreturns the result.jobs.waitreturns the final-stateJob(for the structure, see Response).
Click to view complete example
import os
import json
import dashscope
# If you have not configured the API Key in an environment variable, replace the following line with your own API Key: api_key="sk-xxx"
# China (Beijing): Replace {WorkspaceId} with your actual workspace ID. The region value varies by region.
client = dashscope.Client(
api_key=os.getenv("DASHSCOPE_API_KEY"),
workspace="{WorkspaceId}",
region="cn-beijing",
)
# Submit the recorded file recognition task and wait synchronously for the task to complete
job = client.audio.asr.transcribe(
['{YOUR_AUDIO_URL}'],
model='paraformer-v2',
parameters={'language_hints': ['zh', 'en']} # The "language_hints" parameter only supports the paraformer-v2 model.
)
print(json.dumps(job.model_dump(), indent=4, ensure_ascii=False))
print('transcription done!')
dashscope CLI
# Submit a recorded file recognition task
export DASHSCOPE_API_KEY="your-api-key"
# Replace {WorkspaceId} with your Workspace ID, and cn-beijing with the corresponding region (Singapore: ap-southeast-1, US East: us-east-1)
export DASHSCOPE_HTTP_BASE_URL="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1"
dashscope transcription create -m paraformer-v2 --file-url "https://example.com/audio.mp3"
# Query the task list
dashscope transcription list
ImportantThe SDK Expert interactive assistant can accomplish the same development and troubleshooting in natural language. See
DashScope SDK Expert.For the complete region table, see Base URL overview.
Async submission + async polling
-
Call the core resource (
client.audio.asr) (jobs.create/transcribe) and set the request parameters.Note
- The file transcription service processes tasks submitted through the API on a best-effort basis. After submission, a task enters the queued (
PENDING) state. The queue time depends on the queue length and file duration and cannot be precisely estimated, but typically completes within a few minutes. Once processing begins, speech recognition completes at several hundred times the real-time speed. - After each task completes, the recognition result and URL download link are valid for 24 hours. After expiration, you cannot query the task or download results through the previously provided URL.
- The file transcription service processes tasks submitted through the API on a best-effort basis. After submission, a task enters the queued (
-
Poll the core resource (
client.audio.asr) (jobs.fetch) until the task completes.Stop polling when the status is
SUCCEEDEDorFAILEDand process the result.jobs.fetchreturns aJob(for the structure, see Response).
Click to view complete example
import os
import json
import dashscope
# China (Beijing): Replace {WorkspaceId} with your actual workspace ID. The region value varies by region.
client = dashscope.Client(
# If you have not configured the API Key in an environment variable, replace the following line with your own API Key: api_key="sk-xxx"
api_key=os.getenv("DASHSCOPE_API_KEY"),
workspace="{WorkspaceId}",
region="cn-beijing",
)
# Submit the recorded file recognition task
job = client.audio.asr.jobs.create(
model='paraformer-v2',
file_urls=['{YOUR_AUDIO_URL}'],
parameters={'language_hints': ['zh', 'en']} # The "language_hints" parameter only supports the paraformer-v2 model.
)
# Poll the task until the final result is returned
while True:
if job.status == 'SUCCEEDED' or job.status == 'FAILED':
break
job = client.audio.asr.jobs.fetch(job.task_id)
print(json.dumps(job.model_dump(), indent=4, ensure_ascii=False))
print('transcription done!')
Request parameters
Pass these parameters to the jobs.create/transcribe method of the core resource (client.audio.asr).
| Parameter | Type | Default | Required | Description |
|---|---|---|---|---|
model | str | Yes | The model name for Paraformer audio and video file transcription. Supported models. | |
file_urls | list[str] | Yes | A list of URLs for audio and video file transcription. The HTTP and HTTPS protocols are supported. A single request supports only 1 URL. If audio files are stored in Alibaba Cloud OSS, the SDK does not support temporary URLs with the oss:// prefix. | |
vocabulary_id | str | No | The custom vocabulary ID. Supported for v2 series models; requires language configuration. Disabled by default. Custom Vocabularies. | |
channel_id | list[int] | [0] | No | Specifies the audio track indexes to recognize in a multi-track audio file. Indexes start from 0. For example, [0] means recognizing the first track, and [0, 1] means recognizing both the first and second tracks simultaneously. If this parameter is omitted, only the first track is processed by default. ImportantEach specified track is billed independently. For example, requesting [0, 1] for a single file incurs two separate charges. |
disfluency_removal_enabled | bool | False | No | Filters filler words. Disabled by default. |
timestamp_alignment_enabled | bool | False | No | Enables timestamp alignment. Disabled by default. |
special_word_filter | str | No | Specifies sensitive words to process during speech recognition and supports setting different processing methods for different sensitive words. If this parameter is not provided, the system uses the built-in sensitive word filtering logic, and words matching the Alibaba Cloud Model Studio sensitive word list in the recognition results will be replaced with If this parameter is provided, the following sensitive word processing strategies can be implemented:
The value of this parameter should be a JSON string with the following structure: JSON field descriptions:
| |
language_hints | list[str] | ["zh", "en"] | No | Specifies the language codes of the speech to be recognized. This parameter is only applicable to the paraformer-v2 model. Supported language codes:
|
diarization_enabled | bool | False | No | Automatic speaker diarization. Disabled by default. Only applicable to mono audio. Multi-channel audio does not support speaker diarization. When this feature is enabled, the recognition results will include a NoteIf speaker diarization is enabled, it is recommended that the audio duration does not exceed 2 hours, otherwise recognition may fail or time out. For an example of |
speaker_count | int | No | Reference speaker count. Integer from 2 to 100. Takes effect only when Determined automatically by default. Setting this parameter guides the algorithm but does not guarantee the exact count. |
Response
Job
A Job contains the task basic information (task_id and status) and the execution result (results, see subtask recognition results, task_metrics, usage). The Job returned by jobs.create after task submission contains only task_id and status; the Job returned by jobs.fetch, jobs.wait (and the one-step transcribe) contains the task execution result.
Click to view Job structure examples
The Job returned by jobs.create is as follows (the task is in the PENDING state after submission):
{
"request_id":"251aceab-a6aa-9fc4-b7f7-0cc6d3e2a9f3",
"task_id":"7d0a58a3-1dbe-4de9-8cff-5f48213128b0",
"status":"PENDING"
}
The Job returned by jobs.fetch, jobs.wait (and the one-step transcribe) is as follows:
{
"request_id":"251aceab-a6aa-9fc4-b7f7-0cc6d3e2a9f3",
"task_id":"7d0a58a3-1dbe-4de9-8cff-5f48213128b0",
"status":"PENDING",
"task_metrics":{
"TOTAL":1,
"SUCCEEDED":0,
"FAILED":0
}
}
{
"request_id":"d9d530f1-853c-9848-a5f1-f5de59086ff7",
"task_id":"6351feef-9694-45d2-9d32-63454f2ffb8d",
"status":"RUNNING",
"task_metrics":{
"TOTAL":1,
"SUCCEEDED":0,
"FAILED":0
}
}
{
"request_id":"16668704-6702-9e03-8ab7-a32a5d7bb095",
"task_id":"6351feef-9694-45d2-9d32-63454f2ffb8d",
"status":"SUCCEEDED",
"results":[
{
"file_url":"{YOUR_AUDIO_URL}",
"transcription_url":"https://dashscope-result-bj.oss-cn-beijing.aliyuncs.com/prod/paraformer-v2/20250213/17%3A31/20ee4e4f-0404-4806-b617-c7d4c62eed19-1.json?Expires=1739525481&OSSAccessKeyId=YOUR_ACCESS_KEY_ID&Signature=YOUR_SIGNATURE",
"subtask_status":"SUCCEEDED"
}
],
"task_metrics":{
"TOTAL":1,
"SUCCEEDED":1,
"FAILED":0
},
"usage":{
"duration":9
}
}
{
"request_id":"16668704-6702-9e03-8ab7-a32a5d7bb095",
"task_id":"7bac899c-06ec-4a79-8875-xxxxxxxxxxxx",
"status":"SUCCEEDED",
"results":[
{
"file_url":"{YOUR_AUDIO_URL}",
"code":"InvalidFile.DownloadFailed",
"message":"The audio file cannot be downloaded.",
"subtask_status":"FAILED"
}
],
"task_metrics":{
"TOTAL":1,
"SUCCEEDED":0,
"FAILED":1
},
"usage":{
"duration":9
}
}
Key parameters:
Parameter | Description |
|---|---|
request_id | The ID generated by the system to identify this call. |
task_id | The task ID. |
status | The task status. The four statuses are When a task contains multiple subtasks, if any subtask succeeds, the entire task status is marked as |
results | The recognition results of the subtasks. Each item contains |
subtask_status | The subtask status. The four statuses are |
file_url | The URL of the audio file to be recognized. |
transcription_url | The URL corresponding to the audio recognition result. The recognition result is saved as a JSON file. Download the file from the URL in |
usage | The metering information, including |
When a call fails, the Python SDK raises a DashScopeError exception. You can obtain the error code, HTTP status code, error message, and request ID via e.code/e.status_code/e.message/e.request_id. A subtask failure does not raise an exception; check subtask_status, code, and message in results instead.
results (subtask recognition results)
The results array holds the subtask recognition result for each audio file in the Job. Each item contains file_url, transcription_url, and subtask_status; when a subtask fails, it also contains code and message.
Click to view results structure examples
SUCCEEDED status
{
"file_url":"{YOUR_AUDIO_URL}",
"transcription_url":"https://dashscope-result-bj.oss-cn-beijing.aliyuncs.com/prod/paraformer-v2/20250213/17%3A59/70e737cc-bf8c-418b-b0c8-83fab192a0fa-1.json?Expires=1739527168&OSSAccessKeyId=YOUR_ACCESS_KEY_ID&Signature=YOUR_SIGNATURE",
"subtask_status":"SUCCEEDED"
}
FAILED status
code is the error code and message is the error message, returned only when a subtask fails. Use these two fields with Error codes to troubleshoot.
{
"file_url": "{YOUR_AUDIO_URL}",
"code": "InvalidFile.DownloadFailed",
"message": "The audio file cannot be downloaded.",
"subtask_status": "FAILED"
}
Important parameters:
Parameter | Description |
|---|---|
code | The error code. Use with the |
message | The error message. Use with the |
subtask_status | The subtask status. The four statuses are |
file_url | The URL of the audio file to be recognized. |
transcription_url | The URL corresponding to the audio recognition result. The recognition result is saved in a JSON file. Download the file from |
Recognition result description
The recognition result is saved as a JSON file.
Click to view recognition result example
{
"file_url":"{YOUR_AUDIO_URL}",
"properties":{
"audio_format":"pcm_s16le",
"channels":[
0
],
"original_sampling_rate":16000,
"original_duration_in_milliseconds":3834
},
"transcripts":[
{
"channel_id":0,
"content_duration_in_milliseconds":3720,
"text":"Hello world, this is the Alibaba speech laboratory.",
"sentences":[
{
"begin_time":100,
"end_time":3820,
"text":"Hello world, this is the Alibaba speech laboratory.",
"sentence_id":1,
"speaker_id":0, //This field is only displayed when automatic speaker diarization is enabled
"words":[
{
"begin_time":100,
"end_time":596,
"text":"Hello ",
"punctuation":""
},
{
"begin_time":596,
"end_time":844,
"text":"world",
"punctuation":", "
}
// Other content omitted here
]
}
]
}
]
}
The key parameters are as follows:
Parameter | Type | Description |
|---|---|---|
audio_format | string | The audio format of the source file. |
channels | array[integer] | The audio track index information of the source file. Returns [0] for mono audio, [0, 1] for dual-track audio, and so on. |
original_sampling_rate | integer | The sampling rate (Hz) of the audio in the source file. |
original_duration | integer | The original audio duration (ms) of the source file. |
channel_id | integer | The audio track index of the transcription result, starting from 0. |
content_duration | integer | The duration (ms) of content identified as speech in the audio track. The Paraformer speech recognition model service only transcribes and meters content identified as speech in the audio track, and bills accordingly. Non-speech content is neither metered nor billed. Typically, the speech content duration is shorter than the original audio duration. Since the determination of whether speech content exists is made by an AI model, there may be some deviation from the actual situation. |
transcript | string | The paragraph-level speech transcription result. |
sentences | array | The sentence-level speech transcription result. |
words | array | The word-level speech transcription result. |
begin_time | integer | The start timestamp (ms). |
end_time | integer | The end timestamp (ms). |
text | string | The speech transcription result. |
speaker_id | integer | The index of the current speaker, starting from 0, used to distinguish different speakers. This field is only displayed in the recognition results when speaker diarization is enabled. |
punctuation | string | The predicted punctuation after the word (if any). |
API reference
Core resource (client.audio.asr)
The non-real-time speech recognition capability is mounted on the client.audio.asr resource. You can use client.audio.asr.transcribe to submit and wait in one step, or use client.audio.asr.jobs for step-by-step control.
| Member method | Method signature | Description |
|---|---|---|
transcribe | | Submits a recorded file recognition task and blocks the current thread until the task completes (status is This method returns a |
jobs.create | | Asynchronously submits a speech recognition task. This method returns a |
jobs.wait | | Blocks the current thread until the asynchronous task completes (exponential-backoff polling, default total budget of 24 hours). This method returns the final-state |
jobs.fetch | | Asynchronously queries the current task execution result. This method returns a |
Other interfaces: Batch query task status / Cancel task
For details, see Manage asynchronous tasks: Supports batch querying of non-real-time speech recognition tasks submitted within the last 24 hours, and also supports canceling tasks in PENDING (queued) state.
Error codes
If you encounter errors, see Error codes for troubleshooting.
If the issue persists, join the developer community to report the issue and provide the Request ID for further investigation.
When a task contains multiple subtasks, as long as any subtask succeeds, the overall task status is marked as SUCCEEDED. You need to check the subtask_status field to determine the result of each subtask.
Error response example:
{
"request_id": "16668704-6702-9e03-8ab7-a32a5d7bb095",
"task_id": "7bac899c-06ec-4a79-8875-xxxxxxxxxxxx",
"status": "SUCCEEDED",
"results": [
{
"file_url": "{YOUR_AUDIO_URL}",
"code": "InvalidFile.DownloadFailed",
"message": "The audio file cannot be downloaded.",
"subtask_status": "FAILED"
}
],
"task_metrics": {
"TOTAL": 1,
"SUCCEEDED": 0,
"FAILED": 1
}
}
More examples
Explore more examples on GitHub.
FAQ
Features
Q: Does it support Base64-encoded audio?
No. Base64-encoded audio is not supported. Only audio accessible via publicly accessible URLs is supported. Binary streams and direct local file recognition are not supported.
Q: How to provide audio files as publicly accessible URLs?
Generally, follow these steps (this provides a general approach; specifics vary by storage product. We recommend uploading audio to Alibaba Cloud OSS):
1. Choose a storage and hosting method
For example:
-
Object Storage Service (recommended):
- Use a cloud provider's object storage service (such as Alibaba Cloud OSS) to upload audio files to a bucket and set them to public access.
- Advantages: High availability, CDN acceleration support, easy management.
-
Web server:
- Place audio files on a web server that supports HTTP/HTTPS access (such as Nginx or Apache).
- Advantages: Suitable for small projects or local testing.
-
Content Delivery Network (CDN):
- Host audio files on a CDN and access them through the CDN-provided URL.
- Advantages: Accelerated file delivery, suitable for high-concurrency scenarios.
2. Upload audio files
Upload audio files based on your chosen storage/hosting method, for example:
-
Object Storage Service:
- Log in to the cloud provider's console and create a bucket.
- Upload audio files and set file permissions to "public read" or generate temporary access links.
-
Web server:
- Place audio files in the server's designated directory (such as
/var/www/html/audio/). - Ensure files are accessible via HTTP/HTTPS.
- Place audio files in the server's designated directory (such as
3. Generate a publicly accessible URL
For example:
-
Object Storage Service:
- After uploading, the system automatically generates a public access URL (typically in the format
https://<bucket-name>.<region>.aliyuncs.com/<file-name>). - If you need a more user-friendly domain, you can bind a custom domain and enable HTTPS.
- After uploading, the system automatically generates a public access URL (typically in the format
-
Web server:
- The file access URL is typically the server address plus the file path (such as
https://your-domain.com/audio/file.mp3).
- The file access URL is typically the server address plus the file path (such as
-
CDN:
- After configuring CDN acceleration, use the CDN-provided URL (such as
https://cdn.your-domain.com/audio/file.mp3).
- After configuring CDN acceleration, use the CDN-provided URL (such as
4. Verify URL accessibility
In a public network environment, ensure the generated URL is accessible, for example:
- Open the URL in a browser and check whether the audio file can be played.
- Use tools (such as
curlor Postman) to verify whether the URL returns a correct HTTP response (status code 200).
When using the SDK, if audio files are stored in Alibaba Cloud OSS, temporary URLs with the oss:// prefix are not supported.
When using the RESTful API, if audio files are stored in Alibaba Cloud OSS, temporary URLs with the oss:// prefix are supported:
- The temporary URL is valid for 48 hours and cannot be used after it expires. Do not use it in a production environment.
- The API for obtaining an upload credential is limited to 100 QPS and does not support scaling out. Do not use it in production environments, high-concurrency scenarios, or stress testing scenarios.
- For production environments, use a stable storage service such as OSS to ensure long-term file availability and avoid rate limiting issues.
Q: How long does it take to get recognition results?
After submission, the task enters a queued (PENDING) state. The queue time depends on the queue length and file duration and cannot be precisely estimated, but typically completes within a few minutes. Please wait patiently. Longer audio files require more processing time.
Troubleshooting
For code errors, see Error codes.
Q: What should I do if the recognition results are out of sync with the audio playback?
Set the request parameter timestamp_alignment_enabled to true to enable timestamp calibration, which synchronizes the recognition results with the speech playback.
Q: How do I handle special characters in a file URL?
If the file URL path contains spaces, Chinese characters, or other characters that require encoding, percent-encode those characters before passing the URL to file_urls. For example, encode spaces in the file name as %20. Do not encode an already encoded URL again.
Q: Unable to get results after continuous polling?
This may be due to rate limiting. Please wait patiently. If you need capacity expansion, join the developer community to apply.
Q: Why is there no recognition result (unable to recognize speech)?
- Check whether the audio meets the requirements (format, sampling rate).
- If you are using the
paraformer-v2model, check whether thelanguage_hintssetting is correct. - If none of the above resolves the issue, you can customize hot words to improve the recognition of specific words.
More questions
Visit the QA page on GitHub.