| Whisper V3 Turbo API & Playground |
Fireworks AI |
===============
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Whisper large-v3-turbo is a finetuned version of a pruned Whisper large-v3. In other words, it’s the exact same model, except that the number of decoding layers have reduced from 32 to 4. As a result, the model is way faster, at the expense of a minor quality degradation.
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<CardGroup cols={1}>
Send a sample audio to get a transcription.
</CardGroup>
The input audio file to transcribe or an URL to the public audio file.
Max audio file size is 1 GB, there is no limit for audio duration. Common file formats such as mp3, flac, and wav are supported. Note that the audio will be resampled to 16kHz, downmixed to mono, and reformatted to 16-bit signed little-endian format before transcription. Pre-converting the file before sending it to the API can improve runtime performance.
String name of the ASR model to use. Can be one of `whisper-v3` or `whisper-v3-turbo`. Please use the following serverless endpoints:
* [https://audio-prod.us-virginia-1.direct.fireworks.ai](https://audio-prod.us-virginia-1.direct.fireworks.ai) (for `whisper-v3`);
* [https://audio-turbo.us-virginia-1.direct.fireworks.ai](https://audio-turbo.us-virginia-1.direct.fireworks.ai) (for `whisper-v3-turbo`);
String name of the voice activity detection (VAD) model to use. Can be one of `silero`, or `whisperx-pyannet`.
String name of the alignment model to use. Currently supported:
* `mms_fa` optimal accuracy for multilingual speech.
* `tdnn_ffn` optimal accuracy for English-only speech.
* `gentle` best accuracy for English-only speech (requires a dedicated endpoint, contact us at [inquiries@fireworks.ai](mailto:inquiries@fireworks.ai)).
The target language for transcription. See the [Supported Languages](#supported-languages) section below for a complete list of available languages.
The input prompt that the model will use when generating the transcription. Can be used to specify custom words or specify the style of the transcription. E.g. `Um, here's, uh, what was recorded.` will make the model to include the filler words into the transcription.
Sampling temperature to use when decoding text tokens during transcription. Alternatively, fallback decoding can be enabled by passing a list of temperatures like `0.0,0.2,0.4,0.6,0.8,1.0`. This can help to improve performance.
The format in which to return the response. Can be one of `json`, `text`, `srt`, `verbose_json`, or `vtt`.
The timestamp granularities to populate for this transcription. `response_format` must be set `verbose_json` to use timestamp granularities. Either or both of these options are supported. Can be one of `word`, `segment`, or `word,segment`. If not present, defaults to `segment`.
Whether to get speaker diarization for the transcription. Can be one of `true`, or `false`. If not present, defaults to `false`.
Enabling diarization also requires other fields to hold specific values:
1. `response_format` must be set `verbose_json`.
2. `timestamp_granularities` must include `word` to use diarization.
The minimum number of speakers to detect for diarization. `diarize` must be set `true` to use `min_speakers`. If not present, defaults to `1`.
The maximum number of speakers to detect for diarization. `diarize` must be set `true` to use `max_speakers`. If not present, defaults to `inf`.
Audio preprocessing mode. Currently supported:
* `none` to skip audio preprocessing.
* `dynamic` for arbitrary audio content with variable loudness.
* `soft_dynamic` for speech intense recording such as podcasts and voice-overs.
* `bass_dynamic` for boosting lower frequencies;
The task which was performed. Either `transcribe` or `translate`.
The language of the transcribed/translated text.
The duration of the transcribed/translated audio, in seconds.
The transcribed/translated text.
Extracted words and their corresponding timestamps.
The text content of the word.
The language of the word.
The probability of the word.
The hallucination score of the word.
Start time of the word in seconds.
End time of the word in seconds.
Speaker label for the word.
Segments of the transcribed/translated text and their corresponding details.
The id of the segment.
The text content of the segment.
Start time of the segment in seconds.
End time of the segment in seconds.
Speaker label for the segment.
Extracted words in the segment.
```curl curl
# Download audio file
curl -L -o "audio.flac" "https://tinyurl.com/4997djsh"
# Make request
curl -X POST "https://audio-prod.us-virginia-1.direct.fireworks.ai/v1/audio/transcriptions" \
-H "Authorization: " \
-F "file=@audio.flac"
```
```python fireworks sdk
!pip install fireworks-ai requests python-dotenv
from fireworks.client.audio import AudioInference
import requests
import os
from dotenv import load_dotenv
import time
# Create a .env file with your API key
load_dotenv()
# Download audio sample
audio = requests.get("https://tinyurl.com/4cb74vas").content
# Prepare client
client = AudioInference(
model="whisper-v3",
base_url="https://audio-prod.us-virginia-1.direct.fireworks.ai",
# Or for the turbo version
# model="whisper-v3-turbo",
# base_url="https://audio-turbo.us-virginia-1.direct.fireworks.ai",
api_key=os.getenv("FIREWORKS_API_KEY"),
)
# Make request
start = time.time()
r = await client.transcribe_async(audio=audio)
print(f"Took: {(time.time() - start):.3f}s. Text: '{r.text}'")
```
```python Python (openai sdk)
!pip install openai requests python-dotenv
from openai import OpenAI
import os
import requests
from dotenv import load_dotenv
load_dotenv()
client = OpenAI(
base_url="https://audio-prod.us-virginia-1.direct.fireworks.ai/v1",
api_key=os.getenv("FIREWORKS_API_KEY")
)
audio_file= requests.get("https://tinyurl.com/4cb74vas").content
transcription = client.audio.transcriptions.create(
model="whisper-v3",
file=audio_file
)
print(transcription.text)
```
</RequestExample>
### Supported Languages
The following languages are supported for transcription:
| Language Code | Language Name |
| ------------- | ------------------- |
| en | English |
| zh | Chinese |
| de | German |
| es | Spanish |
| ru | Russian |
| ko | Korean |
| fr | French |
| ja | Japanese |
| pt | Portuguese |
| tr | Turkish |
| pl | Polish |
| ca | Catalan |
| nl | Dutch |
| ar | Arabic |
| sv | Swedish |
| it | Italian |
| id | Indonesian |
| hi | Hindi |
| fi | Finnish |
| vi | Vietnamese |
| he | Hebrew |
| uk | Ukrainian |
| el | Greek |
| ms | Malay |
| cs | Czech |
| ro | Romanian |
| da | Danish |
| hu | Hungarian |
| ta | Tamil |
| no | Norwegian |
| th | Thai |
| ur | Urdu |
| hr | Croatian |
| bg | Bulgarian |
| lt | Lithuanian |
| la | Latin |
| mi | Maori |
| ml | Malayalam |
| cy | Welsh |
| sk | Slovak |
| te | Telugu |
| fa | Persian |
| lv | Latvian |
| bn | Bengali |
| sr | Serbian |
| az | Azerbaijani |
| sl | Slovenian |
| kn | Kannada |
| et | Estonian |
| mk | Macedonian |
| br | Breton |
| eu | Basque |
| is | Icelandic |
| hy | Armenian |
| ne | Nepali |
| mn | Mongolian |
| bs | Bosnian |
| kk | Kazakh |
| sq | Albanian |
| sw | Swahili |
| gl | Galician |
| mr | Marathi |
| pa | Punjabi |
| si | Sinhala |
| km | Khmer |
| sn | Shona |
| yo | Yoruba |
| so | Somali |
| af | Afrikaans |
| oc | Occitan |
| ka | Georgian |
| be | Belarusian |
| tg | Tajik |
| sd | Sindhi |
| gu | Gujarati |
| am | Amharic |
| yi | Yiddish |
| lo | Lao |
| uz | Uzbek |
| fo | Faroese |
| ht | Haitian Creole |
| ps | Pashto |
| tk | Turkmen |
| nn | Nynorsk |
| mt | Maltese |
| sa | Sanskrit |
| lb | Luxembourgish |
| my | Myanmar |
| bo | Tibetan |
| tl | Tagalog |
| mg | Malagasy |
| as | Assamese |
| tt | Tatar |
| haw | Hawaiian |
| ln | Lingala |
| ha | Hausa |
| ba | Bashkir |
| jw | Javanese |
| su | Sundanese |
| yue | Cantonese |
| zh-hant | Traditional Chinese |
| zh-hans | Simplified Chinese |
--
# Create Batch Request
<CardGroup cols={1}>
Create a batch request for our audio transcription service
</CardGroup>
### Headers
Your Fireworks API key, e.g. `Authorization=FIREWORKS_API_KEY`. Alternatively, can be provided as a query param.
### Path Parameters
The relative route of the target API operation (e.g. `"v1/audio/transcriptions"`, `"v1/audio/translations"`). This should correspond to a valid route supported by the backend service.
### Query Parameters
Identifies the target backend service or model to handle the request. Currently supported:
* `audio-prod`: [https://audio-prod.us-virginia-1.direct.fireworks.ai](https://audio-prod.us-virginia-1.direct.fireworks.ai)
* `audio-turbo`: [https://audio-turbo.us-virginia-1.direct.fireworks.ai](https://audio-turbo.us-virginia-1.direct.fireworks.ai)
### Body
Request body fields vary depending on the selected `endpoint_id` and `path`.
The request body must conform to the schema defined by the corresponding synchronous API.\
For example, transcription requests typically accept fields such as `model`, `diarize`, and `response_format`.\
Refer to the relevant synchronous API for required fields:
* [Transcribe audio](https://docs.fireworks.ai/api-reference/audio-transcriptions)
* [Translate audio](https://docs.fireworks.ai/api-reference/audio-translations)
### Response
The status of the batch request submission.\
A value of `"submitted"` indicates the batch request was accepted and queued for processing.
A unique identifier assigned to the batch job.
This ID can be used to check job status or retrieve results later.
The unique identifier of the account associated with the batch job.
The backend service selected to process the request.\
This typically matches the `endpoint_id` used during submission.
A human-readable message describing the result of the submission.\
Typically `"Request submitted successfully"` if accepted.
```curl curl
# Download audio file
curl -L -o "audio.flac" "https://tinyurl.com/4997djsh"
# Make request
curl -X POST "https://audio-batch.link.fireworks.ai/v1/audio/transcriptions?endpoint_id=audio-prod" \
-H "Authorization: " \
-F "file=@audio.flac"
```
```python python
!pip install requests
import os
import requests
# input API key and download audio
api_key = ""
audio = requests.get("https://tinyurl.com/4cb74vas").content
# Prepare request data
url = "https://audio-batch.link.fireworks.ai/v1/audio/transcriptions?endpoint_id=audio-prod"
headers = {"Authorization": api_key}
payload = {
"model": "whisper-v3",
"response_format": "json"
}
files = {"file": ("audio.flac", audio, "audio/flac")}
# Send request
response = requests.post(url, headers=headers, data=payload, files=files)
print(response.text)
```
</RequestExample>
To check the status of your batch request, use the [Check Batch Status](https://docs.fireworks.ai/api-reference/get-batch-status) endpoint with the returned `batch_id`.
--
# Check Batch Status
This endpoint allows you to check the current status of a previously submitted batch request, and retrieve the final result if available.
<CardGroup cols={1}>
Check status of your batch request
</CardGroup>
### Headers
Your Fireworks API key. e.g. `Authorization=FIREWORKS_API_KEY`. Alternatively, can be provided as a query param.
### Path Parameters
The identifier of your Fireworks account. Must match the account used when the batch request was submitted.
The unique identifier of the batch job to check.\
This should match the `batch_id` returned when the batch request was originally submitted.
### Response
The response includes the status of the batch job and, if completed, the final result.
The status of the batch job at the time of the request.\
Possible values include `"completed"` and `"processing"`.
The unique identifier of the batch job whose status is being retrieved.\
This ID matches the one provided in the original request.
A human-readable message describing the current state of the batch job.\
This field is typically `null` when the job has completed successfully.
The original content type of the response body.\
This value can be used to determine how to parse the string in the `body` field.
The serialized result of the batch job, this field is only present when `status` is `"completed"`.\
The format of this string depends on the `content_type` field and may vary across endpoints.\
Clients should use `content_type` to determine how to parse or interpret the value.
```curl curl
# Make request
curl -X GET "https://audio-batch.link.fireworks.ai/v1/accounts/{account_id}/batch_job/{batch_id}" \
-H "Authorization: "
```
```python python
!pip install requests
import os
import requests
# Input api key and path parameters
api_key = ""
account_id = ""
batch_id = ""
# Send request
url = f"https://audio-batch.link.fireworks.ai/v1/accounts/{account_id}/batch_job/{batch_id}"
headers = {"Authorization": api_key}
response = requests.get(url, headers=headers)
print(response.text)
```
</RequestExample>
URL: https://ib.bsb.br/fireworks-ai-whisper-api