faster-whisper-bsc-large-v3-cat

10
2
3.0B
1 language
license:apache-2.0
by
BSC-LT
Audio Model
OTHER
3B params
New
10 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
7GB+ RAM
Mobile
Laptop
Server
Quick Summary

- Model Description - Intended Uses and Limitations - How to Get Started with the Model - Conversion Details - Citation - Additional information The "faster-wh...

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
3GB+ RAM

Code Examples

Installationbash
python -m venv /path/to/venv
Installationbash
python -m venv /path/to/venv
Installationbash
python -m venv /path/to/venv
Installationbash
python -m venv /path/to/venv
Installationbash
python -m venv /path/to/venv
Installationbash
python -m venv /path/to/venv
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source /path/to/venv/bin/activate
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source /path/to/venv/bin/activate
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source /path/to/venv/bin/activate
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source /path/to/venv/bin/activate
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source /path/to/venv/bin/activate
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source /path/to/venv/bin/activate
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pip install faster-whisper
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pip install faster-whisper
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pip install faster-whisper
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pip install faster-whisper
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pip install faster-whisper
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pip install faster-whisper
For Inferencepython
from faster_whisper import WhisperModel

model_size = "BSC-LT/faster-whisper-bsc-large-v3-cat"

# Run on GPU with FP16
model = WhisperModel(model_size, device="cuda", compute_type="float16")

# or run on GPU with INT8
#model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
# or run on CPU with INT8
# model = WhisperModel(model_size, device="cpu", compute_type="int8")

segments, info = model.transcribe("audio_in_catalan.mp3", beam_size=5, task="transcribe",language="ca")

print("Detected language '%s' with probability %f" % (info.language, info.language_probability))

for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
For Inferencepython
from faster_whisper import WhisperModel

model_size = "BSC-LT/faster-whisper-bsc-large-v3-cat"

# Run on GPU with FP16
model = WhisperModel(model_size, device="cuda", compute_type="float16")

# or run on GPU with INT8
#model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
# or run on CPU with INT8
# model = WhisperModel(model_size, device="cpu", compute_type="int8")

segments, info = model.transcribe("audio_in_catalan.mp3", beam_size=5, task="transcribe",language="ca")

print("Detected language '%s' with probability %f" % (info.language, info.language_probability))

for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
For Inferencepython
from faster_whisper import WhisperModel

model_size = "BSC-LT/faster-whisper-bsc-large-v3-cat"

# Run on GPU with FP16
model = WhisperModel(model_size, device="cuda", compute_type="float16")

# or run on GPU with INT8
#model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
# or run on CPU with INT8
# model = WhisperModel(model_size, device="cpu", compute_type="int8")

segments, info = model.transcribe("audio_in_catalan.mp3", beam_size=5, task="transcribe",language="ca")

print("Detected language '%s' with probability %f" % (info.language, info.language_probability))

for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
For Inferencepython
from faster_whisper import WhisperModel

model_size = "BSC-LT/faster-whisper-bsc-large-v3-cat"

# Run on GPU with FP16
model = WhisperModel(model_size, device="cuda", compute_type="float16")

# or run on GPU with INT8
#model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
# or run on CPU with INT8
# model = WhisperModel(model_size, device="cpu", compute_type="int8")

segments, info = model.transcribe("audio_in_catalan.mp3", beam_size=5, task="transcribe",language="ca")

print("Detected language '%s' with probability %f" % (info.language, info.language_probability))

for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
For Inferencepython
from faster_whisper import WhisperModel

model_size = "BSC-LT/faster-whisper-bsc-large-v3-cat"

# Run on GPU with FP16
model = WhisperModel(model_size, device="cuda", compute_type="float16")

# or run on GPU with INT8
#model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
# or run on CPU with INT8
# model = WhisperModel(model_size, device="cpu", compute_type="int8")

segments, info = model.transcribe("audio_in_catalan.mp3", beam_size=5, task="transcribe",language="ca")

print("Detected language '%s' with probability %f" % (info.language, info.language_probability))

for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
For Inferencepython
from faster_whisper import WhisperModel

model_size = "BSC-LT/faster-whisper-bsc-large-v3-cat"

# Run on GPU with FP16
model = WhisperModel(model_size, device="cuda", compute_type="float16")

# or run on GPU with INT8
#model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
# or run on CPU with INT8
# model = WhisperModel(model_size, device="cpu", compute_type="int8")

segments, info = model.transcribe("audio_in_catalan.mp3", beam_size=5, task="transcribe",language="ca")

print("Detected language '%s' with probability %f" % (info.language, info.language_probability))

for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))

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