Merge b76dcf36630a5bd7bbc1851924e23e6128db693f into 517a43ecd132a2089d85f4ebc044728a71d49f6e

examples/test_prob.py

@@ -0,0 +1,78 @@
+import sys
+from pathlib import Path
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+import torch
+import whisper
+import argparse
+import colorsys
+from whisper.utils import exact_div
+from typing import List
+from whisper.tokenizer import get_tokenizer
+from colorama import init, Style
+
+
+
+# hard-coded audio hyperparameters
+SAMPLE_RATE = 16000
+N_FFT = 400
+HOP_LENGTH = 160
+CHUNK_LENGTH = 30
+N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE  # 480000 samples in a 30-second chunk
+N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH)  # 3000 frames in a mel spectrogram input
+
+N_SAMPLES_PER_TOKEN = HOP_LENGTH * 2  # the initial convolutions has stride 2
+FRAMES_PER_SECOND = exact_div(SAMPLE_RATE, HOP_LENGTH)  # 10ms per audio frame
+TOKENS_PER_SECOND = exact_div(SAMPLE_RATE, N_SAMPLES_PER_TOKEN)  # 20ms per audio token
+
+
+def load_audio_from_source(audio_source):
+    audio = whisper.load_audio(audio_source)
+    audio = whisper.pad_or_trim(audio)
+    return audio
+
+
+def decode_audio(model, audio, language="en", f16=True):
+    dtype = torch.float16 if f16 else torch.float32
+    # mel = whisper.log_mel_spectrogram(audio).to(model.device)
+    mel = whisper.log_mel_spectrogram(audio, model.dims.n_mels, padding=N_SAMPLES).to(model.device)
+    mel_segment =whisper.pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype)
+
+    print('Decoding audio') # decode the audio
+    options = whisper.DecodingOptions()
+    result = whisper.decode(model, mel_segment, options)
+
+    tokenizer = get_tokenizer(multilingual=model.is_multilingual, language=language, task=options.task)
+
+    text_tokens = [tokenizer.decode([t]) for t in result.tokens]
+
+    return text_tokens, result.token_probs
+
+def get_colored_text(text_tokens: List[int], token_probs: List[float]):
+    init(autoreset=False)  # Initialize colorama with autoreset=True to reset colors after each print
+    output_text = ""
+    for i, (token, prob) in enumerate(zip(text_tokens, token_probs)):
+        # Interpolate between red and green in the HSV color space
+        r, g, b = colorsys.hsv_to_rgb(prob * (1/3), 1, 1)
+        r, g, b = int(r * 255), int(g * 255), int(b * 255)
+        color_code = f"\033[38;2;{r};{g};{b}m"
+        colored_token = f"{color_code}{Style.BRIGHT}{str(token)}{Style.RESET_ALL}"
+        output_text += colored_token
+    return output_text
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--audio', type=str, help='the path of the audio file')
+    parser.add_argument('--model', type=str, default="large", help='The version of the model to be used')
+
+
+    args = parser.parse_args()
+
+    model = args.model
+    audio = args.audio
+
+    # Load model
+    model = whisper.load_model(model)
+    audio = load_audio_from_source(audio_source=audio)
+    text, proba = decode_audio(model=model, audio=audio)
+    print(get_colored_text(text, proba))

whisper/__init__.py

@@ -11,7 +11,7 @@ from tqdm import tqdm
 from .audio import load_audio, log_mel_spectrogram, pad_or_trim
 from .decoding import DecodingOptions, DecodingResult, decode, detect_language
 from .model import ModelDimensions, Whisper
-from .transcribe import transcribe
+from .transcribe import transcribe, stt
 from .version import __version__
 
 _MODELS = {

whisper/decoding.py

@@ -120,6 +120,7 @@ class DecodingResult:
     language: str
     language_probs: Optional[Dict[str, float]] = None
     tokens: List[int] = field(default_factory=list)
+    token_probs: List[float] = field(default_factory=list)
     text: str = ""
     avg_logprob: float = np.nan
     no_speech_prob: float = np.nan
@@ -218,7 +219,7 @@ class TokenDecoder:
         """Initialize any stateful variables for decoding a new sequence"""
 
     def update(
-        self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor
+        self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor, token_probs: Tensor
     ) -> Tuple[Tensor, bool]:
         """Specify how to select the next token, based on the current trace and logits
 
@@ -245,7 +246,7 @@ class TokenDecoder:
         raise NotImplementedError
 
     def finalize(
-        self, tokens: Tensor, sum_logprobs: Tensor
+        self, tokens: Tensor, sum_logprobs: Tensor, token_probs: Tensor
     ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]:
         """Finalize search and return the final candidate sequences
 
@@ -275,7 +276,7 @@ class GreedyDecoder(TokenDecoder):
         self.eot = eot
 
     def update(
-        self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor
+        self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor, token_probs: Tensor
     ) -> Tuple[Tensor, bool]:
         if self.temperature == 0:
             next_tokens = logits.argmax(dim=-1)
@@ -283,19 +284,24 @@ class GreedyDecoder(TokenDecoder):
             next_tokens = Categorical(logits=logits / self.temperature).sample()
 
         logprobs = F.log_softmax(logits.float(), dim=-1)
+        probs = torch.exp(logprobs)
         current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens]
         sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot)
 
         next_tokens[tokens[:, -1] == self.eot] = self.eot
         tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1)
 
-        completed = (tokens[:, -1] == self.eot).all()
-        return tokens, completed
+        current_token_probs = probs[torch.arange(probs.shape[0]), next_tokens]
+        token_probs = torch.cat([token_probs, current_token_probs[:, None]], dim=-1)
 
-    def finalize(self, tokens: Tensor, sum_logprobs: Tensor):
+        completed = (tokens[:, -1] == self.eot).all()
+        return tokens, completed, token_probs
+
+    def finalize(self, tokens: Tensor, sum_logprobs: Tensor, token_probs: Tensor):
         # make sure each sequence has at least one EOT token at the end
         tokens = F.pad(tokens, (0, 1), value=self.eot)
-        return tokens, sum_logprobs.tolist()
+        token_probs = F.pad(token_probs, (0, 1), value=0)
+        return tokens, sum_logprobs.tolist(), token_probs.tolist()
 
 
 class BeamSearchDecoder(TokenDecoder):
@@ -381,7 +387,7 @@ class BeamSearchDecoder(TokenDecoder):
         )
         return tokens, completed
 
-    def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor):
+    def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor, token_probs: Tensor):
         # collect all finished sequences, including patience, and add unfinished ones if not enough
         sum_logprobs = sum_logprobs.cpu()
         for i, sequences in enumerate(self.finished_sequences):
@@ -682,6 +688,7 @@ class DecodingTask:
         sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device)
         no_speech_probs = [np.nan] * n_batch
 
+        token_probs = torch.zeros_like(tokens).float()
         try:
             for i in range(self.sample_len):
                 logits = self.inference.logits(tokens, audio_features)
@@ -700,14 +707,14 @@ class DecodingTask:
                     logit_filter.apply(logits, tokens)
 
                 # expand the tokens tensor with the selected next tokens
-                tokens, completed = self.decoder.update(tokens, logits, sum_logprobs)
+                tokens, completed, token_probs = self.decoder.update(tokens, logits, sum_logprobs, token_probs)
 
                 if completed or tokens.shape[-1] > self.n_ctx:
                     break
         finally:
             self.inference.cleanup_caching()
 
-        return tokens, sum_logprobs, no_speech_probs
+        return tokens, sum_logprobs, no_speech_probs, token_probs
 
     @torch.no_grad()
     def run(self, mel: Tensor) -> List[DecodingResult]:
@@ -734,7 +741,7 @@ class DecodingTask:
         tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device)
 
         # call the main sampling loop
-        tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens)
+        tokens, sum_logprobs, no_speech_probs, token_probs = self._main_loop(audio_features, tokens)
 
         # reshape the tensors to have (n_audio, n_group) as the first two dimensions
         audio_features = audio_features[:: self.n_group]
@@ -745,7 +752,7 @@ class DecodingTask:
         sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group)
 
         # get the final candidates for each group, and slice between the first sampled token and EOT
-        tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs)
+        tokens, sum_logprobs, token_probs = self.decoder.finalize(tokens, sum_logprobs, token_probs)
         tokens: List[List[Tensor]] = [
             [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s]
             for s in tokens
@@ -768,6 +775,7 @@ class DecodingTask:
             audio_features,
             avg_logprobs,
             no_speech_probs,
+            token_probs
         )
         if len(set(map(len, fields))) != 1:
             raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}")
@@ -782,8 +790,9 @@ class DecodingTask:
                 no_speech_prob=no_speech_prob,
                 temperature=self.options.temperature,
                 compression_ratio=compression_ratio(text),
+                token_probs=token_probs[-len(tokens):]
             )
-            for text, language, tokens, features, avg_logprob, no_speech_prob in zip(
+            for text, language, tokens, features, avg_logprob, no_speech_prob, token_probs in zip(
                 *fields
             )
         ]

whisper/transcribe.py

@@ -17,7 +17,7 @@ from .audio import (
     log_mel_spectrogram,
     pad_or_trim,
 )
-from .decoding import DecodingOptions, DecodingResult
+from .decoding import DecodingOptions, DecodingResult, decode
 from .timing import add_word_timestamps
 from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer
 from .utils import (
@@ -514,6 +514,40 @@ def transcribe(
     )
 
 
+def stt(model: "Whisper", 
+        audio: Union[str, np.ndarray, torch.Tensor],
+        language : str = "en", 
+        f16: bool =True):
+    """
+    Transcribe an audio file using Whisper while getting the probability for each token
+
+    Parameters
+    ----------
+    model: Whisper
+        The Whisper model instance
+
+    audio: Union[str, np.ndarray, torch.Tensor]
+        The path to the audio file to open, or the audio waveform
+    
+    language: string 
+        language used in the audio
+
+    f16: bool
+        check if using torch.float16 otherwise use torch.float32
+    """
+    dtype = torch.float16 if f16 else torch.float32
+    audio = pad_or_trim(audio)
+    mel = log_mel_spectrogram(audio, model.dims.n_mels, padding=N_SAMPLES).to(model.device)
+    mel_segment =pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype)
+
+    options = DecodingOptions()
+    result = decode(model, mel_segment, options)
+    tokenizer = get_tokenizer(multilingual=model.is_multilingual, language=language, task=options.task)
+    text = [tokenizer.decode([t]) for t in result.tokens]
+    output = [ [text, prob] for text, prob in zip(text, result.token_probs) ]
+    return output
+
+
 def cli():
     from . import available_models