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abilmansplus
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Model Details
Model Provider
abilmansplus
Model Tree
openai/whisper-large-v3-turbo
Input Modalities
Output Modalities
Supported Functionality
This is a Whisper large-v3-turbo model
fine-tuned on Kazakh Speech Corpus 2 (about 1000 hours of transcribed audio from diverse sources).
After training on the Train partition, it achieved 9.16% WER on the Test partition.
For longer audio (35+ seconds), you can divide them into 30-second chunks, transcribe each chunk separately, and then merge the results.
Example implementation of a transcriber that can handle both short and long audio files:
python
import librosaimport numpy as npimport torchfrom transformers import WhisperProcessor, WhisperForConditionalGeneration class Transcriber: def __init__( self, model_path="abilmansplus/whisper-turbo-ksc2", device="cuda:0", sampling_rate=16_000, language="kazakh", # set to None if audio is not always in Kazakh, it will still do well on Kazakh task="transcribe", num_beams=5, chunk_length_s=30, # chunk duration (seconds) stride_length_s=1 # overlap (seconds) between chunks ): self.processor = WhisperProcessor.from_pretrained( model_path, language=language, task=task ) self.model = WhisperForConditionalGeneration.from_pretrained(model_path) self.model = self.model.to(device) self.sr = sampling_rate self.language=language # language can be None or "kazakh", any of those will work with this model self.task = task self.num_beams=num_beams self.chunk_length_s = chunk_length_s # chunk length in seconds self.stride_length_s = stride_length_s # overlap between chunks in seconds def transcribe(self, audio_path: str) -> str: """transcribes the audio chunk by chunk and merges the results Args: audio_path (str): path to the audio to be transcribed Returns: full_transcription (str): transcription of the entire audio """ speech_array, sampling_rate = librosa.load(audio_path, sr=self.sr) audio_length_s = len(speech_array) / self.sr # If audio is shorter than chunk_length_s, process normally if audio_length_s <= self.chunk_length_s: full_transcription = self._transcribe_chunk(speech_array) return full_transcription # For longer audio, process in chunks chunk_length_samples = int(self.chunk_length_s * self.sr) stride_length_samples = int(self.stride_length_s * self.sr) # Calculate number of chunks num_samples = len(speech_array) num_chunks = max(1, int( 1 + np.ceil( (num_samples - chunk_length_samples) / (chunk_length_samples - stride_length_samples) ) ) ) transcriptions = [] for i in range(num_chunks): # Calculate chunk start and end start = max(0, i * (chunk_length_samples - stride_length_samples)) end = min(num_samples, start + chunk_length_samples) # Get audio chunk chunk = speech_array[start:end] # Transcribe chunk chunk_transcription = self._transcribe_chunk(chunk) transcriptions.append(chunk_transcription) # Combine transcriptions full_transcription = " ".join(transcriptions) return full_transcription def _transcribe_chunk(self, audio_chunk) -> str: # Process inputs inputs = self.processor( audio_chunk, sampling_rate=self.sr, return_tensors="pt" ).input_features.to(self.model.device) # Get forced decoder IDs for language and task forced_decoder_ids = self.processor.get_decoder_prompt_ids( language=self.language, task=self.task ) # The attention mask should be 1 for all positions in the input features attention_mask = torch.ones_like(inputs[:, :, 0]) # Generate transcription with torch.no_grad(): generated_ids = self.model.generate( inputs, forced_decoder_ids=forced_decoder_ids, max_length=448, num_beams=self.num_beams, attention_mask=attention_mask, ) # Decode the generated IDs to text transcription = self.processor.batch_decode( generated_ids, skip_special_tokens=True )[0] return transcriptionimport librosaimport numpy as npimport torchfrom transformers import WhisperProcessor, WhisperForConditionalGeneration class Transcriber: def __init__( self, model_path="abilmansplus/whisper-turbo-ksc2", device="cuda:0", sampling_rate=16_000, language="kazakh", # set to None if audio is not always in Kazakh, it will still do well on Kazakh task="transcribe", num_beams=5, chunk_length_s=30, # chunk duration (seconds) stride_length_s=1 # overlap (seconds) between chunks ): self.processor = WhisperProcessor.from_pretrained( model_path, language=language, task=task ) self.model = WhisperForConditionalGeneration.from_pretrained(model_path) self.model = self.model.to(device) self.sr = sampling_rate self.language=language # language can be None or "kazakh", any of those will work with this model self.task = task self.num_beams=num_beams self.chunk_length_s = chunk_length_s # chunk length in seconds self.stride_length_s = stride_length_s # overlap between chunks in seconds def transcribe(self, audio_path: str) -> str: """transcribes the audio chunk by chunk and merges the results Args: audio_path (str): path to the audio to be transcribed Returns: full_transcription (str): transcription of the entire audio """ speech_array, sampling_rate = librosa.load(audio_path, sr=self.sr) audio_length_s = len(speech_array) / self.sr # If audio is shorter than chunk_length_s, process normally if audio_length_s <= self.chunk_length_s: full_transcription = self._transcribe_chunk(speech_array) return full_transcription # For longer audio, process in chunks chunk_length_samples = int(self.chunk_length_s * self.sr) stride_length_samples = int(self.stride_length_s * self.sr) # Calculate number of chunks num_samples = len(speech_array) num_chunks = max(1, int( 1 + np.ceil( (num_samples - chunk_length_samples) / (chunk_length_samples - stride_length_samples) ) ) ) transcriptions = [] for i in range(num_chunks): # Calculate chunk start and end start = max(0, i * (chunk_length_samples - stride_length_samples)) end = min(num_samples, start + chunk_length_samples) # Get audio chunk chunk = speech_array[start:end] # Transcribe chunk chunk_transcription = self._transcribe_chunk(chunk) transcriptions.append(chunk_transcription) # Combine transcriptions full_transcription = " ".join(transcriptions) return full_transcription def _transcribe_chunk(self, audio_chunk) -> str: # Process inputs inputs = self.processor( audio_chunk, sampling_rate=self.sr, return_tensors="pt" ).input_features.to(self.model.device) # Get forced decoder IDs for language and task forced_decoder_ids = self.processor.get_decoder_prompt_ids( language=self.language, task=self.task ) # The attention mask should be 1 for all positions in the input features attention_mask = torch.ones_like(inputs[:, :, 0]) # Generate transcription with torch.no_grad(): generated_ids = self.model.generate( inputs, forced_decoder_ids=forced_decoder_ids, max_length=448, num_beams=self.num_beams, attention_mask=attention_mask, ) # Decode the generated IDs to text transcription = self.processor.batch_decode( generated_ids, skip_special_tokens=True )[0] return transcription